Monthly_mean_forecast_generation_code_version2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d5cc38e7",
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    "<!--  -->"
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  {
   "cell_type": "markdown",
   "id": "d423a395",
   "metadata": {
    "id": "d423a395"
   },
   "source": [
    "# 1. Importing required libraries "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a432d66",
   "metadata": {
    "id": "7a432d66"
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   "source": [
    "<!--  -->"
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb6bbc71",
   "metadata": {
    "id": "bb6bbc71",
    "outputId": "1df6de38-4555-4578-c0df-c0a07b997191"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-19 12:27:45 prophet.plot ERROR: Importing plotly failed. Interactive plots will not work.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##################################################\n",
      "    Successfully imported required libraries!!\n",
      "##################################################\n"
     ]
    }
   ],
   "source": [
    "from warnings import filterwarnings\n",
    "\n",
    "filterwarnings(action='ignore')\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from IPython.display import clear_output\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os\n",
    "\n",
    "import shutil\n",
    "\n",
    "from darts import TimeSeries\n",
    "\n",
    "from darts.dataprocessing.transformers import Scaler\n",
    "\n",
    "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n",
    "\n",
    "from darts.metrics import mape,mse\n",
    "\n",
    "from darts.models import AutoARIMA, Prophet, RandomForest, LightGBMModel, RNNModel, BlockRNNModel\n",
    "\n",
    "print(50*'#')\n",
    "\n",
    "print(4*' '+\"Successfully imported required libraries!!\")\n",
    "\n",
    "print(50*'#')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c195817d",
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    "id": "c195817d"
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    "<!--  -->"
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   "cell_type": "markdown",
   "id": "819013d8",
   "metadata": {
    "id": "819013d8"
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   "source": [
    "<!--  -->"
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  },
  {
   "cell_type": "markdown",
   "id": "ac957a13",
   "metadata": {
    "id": "ac957a13"
   },
   "source": [
    "# 2. Importing observation data and verifying required specifications"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72797f7d",
   "metadata": {
    "id": "72797f7d"
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   "source": [
    "<!--  -->"
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  },
  {
   "cell_type": "markdown",
   "id": "f494ef42",
   "metadata": {
    "id": "f494ef42"
   },
   "source": [
    "## 2.1 Import "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "46b51059",
   "metadata": {
    "id": "46b51059"
   },
   "outputs": [],
   "source": [
    "def import_observation_windspeed_data(file_name = 'obs_data.csv'):\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    Function imports observation wind speed data provided in csv file \n",
    "    \n",
    "    Input: file_name (str), default: 'obs_data.csv'\n",
    "    \n",
    "    Output: series (pandas DataFrame object) or None object\n",
    "    \n",
    "    \"\"\"\n",
    "    try:\n",
    "\n",
    "        # Importing series data\n",
    "\n",
    "        series = pd.read_csv(file_name,index_col=0,parse_dates=True)\n",
    "\n",
    "        # Sorting based on time index labels\n",
    "\n",
    "        series.sort_index(inplace=True)\n",
    "\n",
    "        # Printing data\n",
    "\n",
    "        series.columns = ['windspeed',]\n",
    "        \n",
    "        return series\n",
    "    \n",
    "    except: \n",
    "                \n",
    "        return None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6518458",
   "metadata": {
    "id": "b6518458"
   },
   "source": [
    "## 2.2 Converting data to daily average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc94ce2e",
   "metadata": {
    "id": "bc94ce2e"
   },
   "outputs": [],
   "source": [
    "def compute_daily_mean(series):\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    Function computes daily mean.\n",
    "    \n",
    "    Input: series (pandas DataFrame object)\n",
    "    \n",
    "    Onput: series_daily_mean: series averaged to daily-mean(pandas DataFrame object)\n",
    "    \n",
    "    \"\"\"\n",
    "    try:\n",
    "        \n",
    "        series_daily_mean = series.resample(rule='D').mean()\n",
    "        \n",
    "        return series_daily_mean\n",
    "    \n",
    "    except:\n",
    "        \n",
    "        return None\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543cbb1b",
   "metadata": {
    "id": "543cbb1b"
   },
   "source": [
    "## 2.3 Verifying required specifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c3dbba5",
   "metadata": {
    "id": "1c3dbba5"
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   "outputs": [],
   "source": [
    "# Function definition\n",
    "\n",
    "def verify_required_specifications(series):\n",
    "    \n",
    "    \"\"\"\n",
    "    Input: series (pandas DataFrame objects). \n",
    "    \n",
    "    Output: \n",
    "            \n",
    "            1. proceed: True or False (Boolean)\n",
    "            \n",
    "            2. error_msg: specifing condition or conditions which are mismatched(str)\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    nmissing = series.iloc[:,0].isna().sum()\n",
    "    \n",
    "    if nmissing == 0:\n",
    "        \n",
    "        # Returning boolean if requirements are verified \n",
    "        \n",
    "        proceed = True\n",
    "        \n",
    "        error_msg = ''\n",
    "        \n",
    "        return proceed, error_msg\n",
    "    \n",
    "    else:\n",
    "        \n",
    "        proceed_lst = []\n",
    "        \n",
    "        error_msg = ''\n",
    "        \n",
    "        # Condition 1: Minimum of 24 months data (continuous)\n",
    "        \n",
    "        n_months = len(series.index.strftime(\"%m-%Y\")) \n",
    "\n",
    "        if n_months < 24:\n",
    "            \n",
    "            proceed_lst.append(False)\n",
    "            \n",
    "            error_msg = error_msg + f\"Condition:1 Total number of months (continuous) >= 24 (failed)\\n\"\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            proceed_lst.append(True)\n",
    "\n",
    "        # Condition 2: maximum of 10% missing values\n",
    "        \n",
    "        percentage_null = (nmissing / len(series)) * 100\n",
    "        \n",
    "        if percentage_null > 10:\n",
    "            \n",
    "            proceed_lst.append(False)\n",
    "            \n",
    "            error_msg = error_msg + f\"Condition:1 Percentage of number of missing values < 10% (failed)\\n\"\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            proceed_lst.append(True)\n",
    "            \n",
    "            \n",
    "        # Condition 3: maximum of 5 days gap\n",
    "\n",
    "        # logic: cum sum value of a non-null value repeats for the next consecutive nan values hence\n",
    "        # repetition of cum sum value is observed, (value_counts)counting total repititions gives us\n",
    "        # length of consecutive nan values + 1, +1 since non null value's count is also included.\n",
    "\n",
    "        max_gap_len = (series.notnull().astype(int).cumsum().value_counts() - 1).max()        \n",
    "\n",
    "        if max_gap_len > 5:\n",
    "            \n",
    "            proceed_lst.append(False)\n",
    "            \n",
    "            error_msg = error_msg + f\"Condition:2 maximum gap <= 5 days (failed)\\n\"\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            proceed_lst.append(True)\n",
    "         \n",
    "        # Condition 4: atleast 15 days of data for each month\n",
    "        \n",
    "        series_mon = series.dropna().resample('MS').mean()\n",
    "        \n",
    "        series_mon['days_count'] = series.dropna().resample('MS').count()\n",
    "        \n",
    "        min_day_count = (len(series_mon.loc[series_mon['days_count'] < 15,:]) > 0 )\n",
    "        \n",
    "        if min_day_count:\n",
    "\n",
    "            proceed_lst.append(False)\n",
    "            \n",
    "            error_msg = error_msg + f\"Condition:3 number of days for each month >= 15 (failed)\\n\"\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            proceed_lst.append(True)\n",
    "                 \n",
    "\n",
    "        if all(proceed_lst):\n",
    "            \n",
    "            return True, ''\n",
    "        \n",
    "        else:\n",
    "            \n",
    "            return False, error_msg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f5c479",
   "metadata": {
    "id": "89f5c479"
   },
   "source": [
    "## 2.4 Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "50c8e3d2",
   "metadata": {
    "id": "50c8e3d2"
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   "outputs": [],
   "source": [
    "# Function definition\n",
    "\n",
    "def fill_missing_values_interpolation(series):\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    Input: series (pandas DataFrame objects). \n",
    "    \n",
    "    Output: series_mod: series with missing values filled(pandas DataFrame objects)\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    nmissing = series.iloc[:,0].isna().sum()\n",
    "    \n",
    "    percentage_null = (nmissing/len(series)) * 100\n",
    "    \n",
    "    if nmissing == 0:\n",
    "                \n",
    "        return series\n",
    "    \n",
    "    else:\n",
    "\n",
    "        series_mod = series.interpolate('time')\n",
    "\n",
    "        #print(f\"{nmissing} missing values found out of {len(series)} data-points, accounting for {percentage_null:3.2f} %\")\n",
    "\n",
    "        #print()\n",
    "\n",
    "        #print(\"filled by interpolation\")\n",
    "        \n",
    "        #print()\n",
    "        \n",
    "        return series_mod\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61fcfe88",
   "metadata": {
    "id": "61fcfe88"
   },
   "source": [
    "## 2.5 Main function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e0a668b",
   "metadata": {
    "id": "6e0a668b"
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   "outputs": [],
   "source": [
    "def main_function_1_import_verify_interpolate():\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    Function imports wind speed data, verifies if the required specifications are satified and interpolates\n",
    "    \n",
    "    if found any nan values.\n",
    "    \n",
    "    Output: pandas DataFrame object or None\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    series_imported = import_observation_windspeed_data(file_name = 'obs_data.csv')\n",
    "    \n",
    "    if series_imported is None:\n",
    "        \n",
    "        print(\"Importing wind speed data failed, please check the observation file name and its data format\")\n",
    "        \n",
    "        print()\n",
    "        \n",
    "        return None\n",
    "    \n",
    "    series_daily_mean = compute_daily_mean(series_imported)\n",
    "    \n",
    "    if series_daily_mean is None:\n",
    "        \n",
    "        print(\"Averaging wind speed data to daily mean failed, please check the frequency of the data\")\n",
    "        \n",
    "        print()\n",
    "        \n",
    "        return None\n",
    "    \n",
    "    proceed, err_msg = verify_required_specifications(series_daily_mean)\n",
    "    \n",
    "    if not proceed:\n",
    "        \n",
    "        print(err_msg)\n",
    "        \n",
    "        print()\n",
    "        \n",
    "        return None\n",
    "    \n",
    "    else:\n",
    "        \n",
    "        series_daily_mean_filled = fill_missing_values_interpolation(series_daily_mean)\n",
    "        \n",
    "        return series_daily_mean_filled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec4ae564",
   "metadata": {
    "id": "ec4ae564"
   },
   "source": [
    "<!--  -->"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d27ae2d7",
   "metadata": {
    "id": "d27ae2d7"
   },
   "source": [
    "<!--  -->"
   ]
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  {
   "cell_type": "markdown",
   "id": "537a9dea",
   "metadata": {
    "id": "537a9dea"
   },
   "source": [
    "# 3. Computing required components for forecast process"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a3cc07",
   "metadata": {
    "id": "69a3cc07"
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   "source": [
    "<!--  -->"
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  },
  {
   "cell_type": "markdown",
   "id": "8d50eb05",
   "metadata": {
    "id": "8d50eb05"
   },
   "source": [
    "## 3.1 Computing monthly-mean and climatology (Long term average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44a383a2",
   "metadata": {
    "id": "44a383a2"
   },
   "outputs": [],
   "source": [
    "def compute_monthly_mean_climatology(series):\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    Function computes month-mean wind speeds and monthly-mean climatology (long term average).\n",
    "    \n",
    "    Input: Daily-mean wind speed (pandas DataFrame object)\n",
    "    \n",
    "    Output: 1. monthly-mean wind speed (pandas DataFrame object)\n",
    "            \n",
    "            2. monthly-mean climatology (pandas DataFrame object)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Monthly-mean\n",
    "\n",
    "    series_mm = series.resample('MS').mean()\n",
    "\n",
    "    # Climatology\n",
    "\n",
    "    climatology = series_mm.copy()\n",
    "\n",
    "    climatology['month'] = climatology.index.month\n",
    "\n",
    "    climatology = climatology.groupby('month').mean()\n",
    "\n",
    "    climatology.columns = ['climatology',]\n",
    "\n",
    "    return series_mm, climatology\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1615f9d",
   "metadata": {
    "id": "f1615f9d"
   },
   "source": [
    "## 3.2 Computing components required for model training and validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d6c1dda",
   "metadata": {
    "id": "2d6c1dda"
   },
   "outputs": [],
   "source": [
    "def compute_cmpnts_required_forcasting(series,plot_train_validation=True):\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    Function computes required components to start forecast process.\n",
    "    \n",
    "    Input: (pandas DataFrame object)\n",
    "    \n",
    "    Output:\n",
    "\n",
    "    train_transformed - train part of target series transformed or normalized(TimeSeries object) \n",
    "\n",
    "    validation_transformed - Validation part of target series transformed or normalized(TimeSeries object)\n",
    "\n",
    "    validation - Validation part of target series(TimeSeries object)\n",
    "\n",
    "    trnsfm - Transform object\n",
    "\n",
    "    covariates - year, month covariates (normalized) stacked together(TimeSeries object)\n",
    "\n",
    "    show_plots - Boolean on whether to display line plots of predictions (default False)\n",
    "\n",
    "    Output: tuple -> (error, min_error, model_code(best model))\n",
    "\n",
    "    error: Validation error produced by 6 different models(Pandas Series object)\n",
    "\n",
    "    \n",
    "    \"\"\"        \n",
    "    # Converting series to timeseries object\n",
    "\n",
    "    series_TS = TimeSeries.from_dataframe(series).astype('float32')\n",
    "\n",
    "    # Dividing into train and validation sets\n",
    "\n",
    "    # train: Series start to 13 th data-point from last\n",
    "\n",
    "    # validation: last 12 months data\n",
    "\n",
    "    train, validation = series_TS[:-12:], series_TS[-12::]\n",
    "\n",
    "    if plot_train_validation:\n",
    "\n",
    "        train.plot(label='train')\n",
    "\n",
    "        validation.plot(label='validation')\n",
    "\n",
    "        plt.xlabel('Month',fontsize=12)\n",
    "\n",
    "        plt.ylabel('Wind speed (m/s)',fontsize=12)    \n",
    "\n",
    "        plt.show();\n",
    "\n",
    "    # Normalizing time series\n",
    "\n",
    "    trnsfm =  Scaler()\n",
    "\n",
    "    train_transformed = trnsfm.fit_transform(train)\n",
    "\n",
    "    validation_transformed = trnsfm.transform(validation)\n",
    "\n",
    "    full_series_transformed = trnsfm.transform(series_TS)\n",
    "\n",
    "    # Defining covariates (month and year) and stacking them\n",
    "\n",
    "    index = pd.date_range(series.index[0],\n",
    "                          series.index[-1] + pd.Timedelta(365,'days'),\n",
    "                          freq='MS')\n",
    "\n",
    "    index_ts = TimeSeries.from_times_and_values(times=index,values=np.zeros(len(index)))\n",
    "\n",
    "    year_covariate = datetime_attribute_timeseries(index_ts,'year')\n",
    "\n",
    "    year_covariate = Scaler().fit_transform(year_covariate)\n",
    "\n",
    "    month_covariate = datetime_attribute_timeseries(index_ts,'month')\n",
    "\n",
    "    month_covariate = Scaler().fit_transform(month_covariate)\n",
    "\n",
    "    covariates = year_covariate.stack(month_covariate).astype('float32')\n",
    "\n",
    "    return trnsfm, validation, train_transformed, validation_transformed, full_series_transformed, covariates\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b899b807",
   "metadata": {
    "id": "b899b807"
   },
   "source": [
    "## 3.3 Main function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4777630f",
   "metadata": {
    "id": "4777630f"
   },
   "outputs": [],
   "source": [
    "def main_function_2_compute_fcst_req_cmpnts(series):\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    Function computes components required for starting forecast process\n",
    "    \n",
    "    Input: Daily-mean wind speed (pandas Series object)\n",
    "    \n",
    "    Output: Components required for forecasting (tuple object)\n",
    "    \n",
    "    \"\"\"\n",
    "        \n",
    "    series_monthly_mean, climatology = compute_monthly_mean_climatology(series)\n",
    "        \n",
    "    components = compute_cmpnts_required_forcasting(series_monthly_mean)\n",
    "    \n",
    "    trnsfm, validation, train_transformed, validation_transformed, full_series_transformed, covariates = components\n",
    "        \n",
    "        \n",
    "    return (series_monthly_mean, climatology,\n",
    "               trnsfm, validation,\n",
    "               train_transformed, validation_transformed,\n",
    "               full_series_transformed, covariates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0febd1d0",
   "metadata": {
    "id": "0febd1d0"
   },
   "outputs": [],
   "source": [
    "# components = main_function_2_compute_fcst_req_cmpnts(series_daily_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94a870cf",
   "metadata": {
    "id": "94a870cf"
   },
   "source": [
    "<!--  -->"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61fb8f6",
   "metadata": {
    "id": "b61fb8f6"
   },
   "source": [
    "# 4. Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fad57c0",
   "metadata": {
    "id": "1fad57c0"
   },
   "source": [
    "<!--  -->"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4d60444",
   "metadata": {
    "id": "c4d60444"
   },
   "source": [
    "## 4.1 Forecasting with default settings and obtaining performance estimates of different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d83cb758",
   "metadata": {
    "id": "d83cb758"
   },
   "outputs": [],
   "source": [
    "def get_forecast_perf_estimates(train_transformed,validation_transformed,\n",
    "                                validation,trnsfm,covariates,\n",
    "                                show_plots=True):\n",
    "    \n",
    "    \"\"\"\n",
    "    Function provides performance estimates of 6 models (2 statistical, 2 ML-Regression based, 2 Sequence based)\n",
    "    \n",
    "    with default settings.\n",
    "    \n",
    "    Input: \n",
    "    \n",
    "    train_transformed - train part of target series transformed or normalized(TimeSeries object) \n",
    "    \n",
    "    validation_transformed - Validation part of target series transformed or normalized(TimeSeries object)\n",
    "    \n",
    "    validation - Validation part of target series(TimeSeries object)\n",
    "    \n",
    "    trnsfm - Transform object\n",
    "    \n",
    "    covariates - year, month covariates (normalized) stacked together(TimeSeries object)\n",
    "    \n",
    "    show_plots - Boolean on whether to display line plots of predictions (default False)\n",
    "    \n",
    "    Output: tuple -> (error, fig)\n",
    "    \n",
    "    error: Validation error produced by 6 different models(Pandas Series object)\n",
    "    \n",
    "    fig: matplotib figure object\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    \n",
    "    # model input steps \n",
    "\n",
    "    i = 12\n",
    "\n",
    "    # model output step\n",
    "\n",
    "    o = 1\n",
    "    \n",
    "    # length of full series\n",
    "    \n",
    "    series_len = len(train_transformed) + len(validation_transformed)\n",
    "    \n",
    "    error = {}\n",
    "\n",
    "    # 1. Statitical models\n",
    "\n",
    "    ## 1.1 AutoARIMA\n",
    "\n",
    "    try:\n",
    "\n",
    "        pred_AA = AutoARIMA(m=12,maxiter=100).fit(train_transformed,future_covariates=covariates).predict(n=12,future_covariates=covariates)\n",
    "\n",
    "    except:\n",
    "\n",
    "        pred_AA = AutoARIMA(maxiter=100).fit(train_transformed,future_covariates=covariates).predict(n=12,future_covariates=covariates)\n",
    "\n",
    "\n",
    "    pred_AA = trnsfm.inverse_transform(pred_AA)\n",
    "\n",
    "    clear_output()\n",
    "\n",
    "    error['AA'] = mape(validation,pred_AA)\n",
    "\n",
    "    ## 1.2 Prophet\n",
    "\n",
    "    pred_PH = Prophet().fit(train_transformed,future_covariates=covariates).predict(n=12,future_covariates=covariates)\n",
    "\n",
    "    pred_PH = trnsfm.inverse_transform(pred_PH)\n",
    "\n",
    "    clear_output()\n",
    "\n",
    "    error['PH'] = mape(validation,pred_PH)\n",
    "\n",
    "    # 2. Regression models\n",
    "\n",
    "    ## 2.1 RandomForest\n",
    "\n",
    "    try:\n",
    "\n",
    "        pred_RF = RandomForest(lags=i,lags_future_covariates=[0,],lags_past_covariates=[-6,-5,-4,-3,-2,-1,]) \\\n",
    "    .fit(train_transformed,future_covariates=covariates,past_covariates=covariates) \\\n",
    "    .predict(n=12,future_covariates=covariates)\n",
    "        \n",
    "    except:\n",
    "\n",
    "        pred_RF = RandomForest(lags=6,lags_future_covariates=[0,],lags_past_covariates=[-6,-5,-4,-3,-2,-1,]) \\\n",
    "    .fit(train_transformed,future_covariates=covariates,past_covariates=covariates) \\\n",
    "    .predict(n=12,future_covariates=covariates)\n",
    "\n",
    "    pred_RF = trnsfm.inverse_transform(pred_RF)\n",
    "\n",
    "    error['RF'] = mape(validation,pred_RF)\n",
    "\n",
    "    ## 2.2 LightGBMModel\n",
    "    try:\n",
    "\n",
    "        pred_LG = LightGBMModel(lags=i,lags_future_covariates=[0,],lags_past_covariates=[-6,-5,-4,-3,-2,-1,],\n",
    "                               min_data_in_bin=1,min_data_in_leaf=1,) \\\n",
    "        .fit(train_transformed,future_covariates=covariates,past_covariates=covariates) \\\n",
    "        .predict(n=12,future_covariates=covariates)\n",
    "\n",
    "    except:\n",
    "\n",
    "        pred_LG = LightGBMModel(lags=6,lags_future_covariates=[0,],lags_past_covariates=[-6,-5,-4,-3,-2,-1,],\n",
    "                               min_data_in_bin=1,min_data_in_leaf=1,) \\\n",
    "        .fit(train_transformed,future_covariates=covariates,past_covariates=covariates) \\\n",
    "        .predict(n=12,future_covariates=covariates)\n",
    "\n",
    "    pred_LG = trnsfm.inverse_transform(pred_LG)\n",
    "\n",
    "    clear_output()\n",
    "\n",
    "    error['LG'] = mape(validation,pred_LG)\n",
    "\n",
    "    # 3. Sequence models\n",
    "\n",
    "    # Sequence models are fitted if length of time series (train + validation) is atleast 4 years\n",
    "\n",
    "    if series_len >= 48:\n",
    "\n",
    "        ## 3.1 RNNModel (GRU)\n",
    "\n",
    "        model_RNN = RNNModel(       \n",
    "            model=\"GRU\",\n",
    "            input_chunk_length=i,\n",
    "            output_chunk_length=1,\n",
    "            hidden_dim=3,\n",
    "            n_rnn_layers=1,\n",
    "            n_epochs=100,\n",
    "            model_name=\"model_RNN\",\n",
    "            save_checkpoints=True,force_reset=True,\n",
    "            )\n",
    "\n",
    "        model_RNN.fit(train_transformed,future_covariates=covariates)\n",
    "\n",
    "        pred_RNN = trnsfm.inverse_transform(model_RNN.predict(n=12,future_covariates=covariates))\n",
    "\n",
    "        clear_output()\n",
    "\n",
    "        error['RNN'] = mape(validation,pred_RNN)\n",
    "\n",
    "        ## 3.2 BlockRNNModel (GRU)\n",
    "\n",
    "        model_BRNN = BlockRNNModel(\n",
    "            model=\"GRU\",\n",
    "            input_chunk_length=i,\n",
    "            output_chunk_length=1,\n",
    "            hidden_size=3,\n",
    "            n_rnn_layers=1,\n",
    "            n_epochs=100,\n",
    "            model_name=\"model_BRNN\",force_reset=True,\n",
    "            save_checkpoints=True)\n",
    "\n",
    "        model_BRNN.fit(train_transformed,past_covariates=covariates)\n",
    "\n",
    "        pred_BRNN = trnsfm.inverse_transform(model_BRNN.predict(n=12,past_covariates=covariates))\n",
    "\n",
    "        clear_output()\n",
    "\n",
    "        error['BRNN'] = mape(validation,pred_BRNN)\n",
    "        \n",
    "        shutil.rmtree('darts_logs/')\n",
    "\n",
    "    error = pd.Series(error)\n",
    "\n",
    "    if show_plots:\n",
    "\n",
    "        if series_len >= 48:\n",
    "\n",
    "            # Plotting\n",
    "\n",
    "            fig, axes = plt.subplots(3,2,figsize=(20,24),sharey='row',sharex='col')\n",
    "\n",
    "            ax1,ax2,ax3,ax4,ax5,ax6 = axes.flat\n",
    "\n",
    "            # 1. Statistical models\n",
    "            validation.plot(ax=ax1,label='actual')\n",
    "            pred_AA.plot(ax=ax1,label='prediction')\n",
    "            ax1.legend(fontsize=24)\n",
    "            ax1.set_title(f'AutoARIMA (MAPE:{error[\"AA\"]:3.2f})',fontsize=30)\n",
    "\n",
    "            validation.plot(ax=ax2,label='actual')    \n",
    "            pred_PH.plot(ax=ax2,label='prediction')\n",
    "            ax2.legend(fontsize=24)\n",
    "            ax2.set_title(f'Prophet(MAPE:{error[\"PH\"]:3.2f})',fontsize=30)\n",
    "\n",
    "            # 2. Regression models\n",
    "            validation.plot(ax=ax3,label='actual')\n",
    "            pred_RF.plot(ax=ax3,label='prediction')\n",
    "            ax3.legend(fontsize=24)\n",
    "            ax3.set_title(f'RandomForest (MAPE:{error[\"RF\"]:3.2f})',fontsize=30)\n",
    "\n",
    "            validation.plot(ax=ax4,label='actual')    \n",
    "            pred_LG.plot(ax=ax4,label='prediction')\n",
    "            ax4.legend(fontsize=24)\n",
    "            ax4.set_title(f'LGBDT (MAPE:{error[\"LG\"]:3.2f})',fontsize=30)\n",
    "\n",
    "            # 3. Sequence models\n",
    "            validation.plot(ax=ax5,label='actual')\n",
    "            pred_RNN.plot(ax=ax5,label='prediction')\n",
    "            ax5.legend(fontsize=24)\n",
    "            ax5.set_title(f'RNN (MAPE:{error[\"RNN\"]:3.2f})',fontsize=30)\n",
    "\n",
    "            validation.plot(ax=ax6,label='actual')    \n",
    "            pred_BRNN.plot(ax=ax6,label='prediction')\n",
    "            ax6.legend(fontsize=24)\n",
    "            ax6.set_title(f'BlockRNN (MAPE:{error[\"BRNN\"]:3.2f})',fontsize=30)\n",
    "            \n",
    "            ax1.set_xlabel('')\n",
    "            ax2.set_xlabel('')\n",
    "            \n",
    "            ax3.set_xlabel('')\n",
    "            ax4.set_xlabel('')\n",
    "\n",
    "            ax5.set_xlabel('Month',fontsize=24)\n",
    "            ax6.set_xlabel('Month',fontsize=24)\n",
    "                        \n",
    "            ax1.set_ylabel('Wind speed (m/s)',fontsize=24)\n",
    "            ax3.set_ylabel('Wind speed (m/s)',fontsize=24)\n",
    "            ax5.set_ylabel('Wind speed (m/s)',fontsize=24)\n",
    "            \n",
    "            ax1.tick_params(labelsize=20)\n",
    "            ax2.tick_params(labelsize=20)\n",
    "            ax3.tick_params(labelsize=20)\n",
    "            ax4.tick_params(labelsize=20)\n",
    "            ax5.tick_params(labelsize=20)\n",
    "            ax6.tick_params(labelsize=20)\n",
    "            \n",
    "        else:\n",
    "\n",
    "            # Plotting\n",
    "\n",
    "            fig, axes = plt.subplots(2,2,figsize=(20,16),sharey='row',sharex='col')\n",
    "\n",
    "            ax1,ax2,ax3,ax4 = axes.flat\n",
    "\n",
    "            # 1. Statistical models\n",
    "            validation.plot(ax=ax1,label='actual')\n",
    "            pred_AA.plot(ax=ax1,label='prediction')\n",
    "            ax1.legend(fontsize=24)\n",
    "            ax1.set_title(f'AutoARIMA (MAPE:{error[\"AA\"]:3.2f})',fontsize=30)\n",
    "\n",
    "            validation.plot(ax=ax2,label='actual')    \n",
    "            pred_PH.plot(ax=ax2,label='prediction')\n",
    "            ax2.legend(fontsize=24)\n",
    "            ax2.set_title(f'Prophet(MAPE:{error[\"PH\"]:3.2f})',fontsize=30)\n",
    "\n",
    "            # 2. Regression models\n",
    "            validation.plot(ax=ax3,label='actual')\n",
    "            pred_RF.plot(ax=ax3,label='prediction')\n",
    "            ax3.legend(fontsize=24)\n",
    "            ax3.set_title(f'RandomForest (MAPE:{error[\"RF\"]:3.2f})',fontsize=30)\n",
    "\n",
    "            validation.plot(ax=ax4,label='actual')    \n",
    "            pred_LG.plot(ax=ax4,label='prediction')\n",
    "            ax4.legend(fontsize=24)\n",
    "            ax4.set_title(f'LGBDT (MAPE:{error[\"LG\"]:3.2f})',fontsize=30)\n",
    "            \n",
    "            ax1.set_xlabel('')\n",
    "            ax2.set_xlabel('')\n",
    "            \n",
    "            ax3.set_xlabel('Month',fontsize=24)\n",
    "            ax4.set_xlabel('Month',fontsize=24)\n",
    "                        \n",
    "            ax1.set_ylabel('Wind speed (m/s)',fontsize=24)\n",
    "            ax3.set_ylabel('Wind speed (m/s)',fontsize=24)\n",
    "            \n",
    "            ax1.tick_params(labelsize=20)\n",
    "            ax2.tick_params(labelsize=20)\n",
    "            ax3.tick_params(labelsize=20)\n",
    "            ax4.tick_params(labelsize=20)\n",
    "            \n",
    "        return error, fig\n",
    "    \n",
    "    else:\n",
    "    \n",
    "        return error, None\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85383886",
   "metadata": {
    "id": "85383886"
   },
   "source": [
    "## 4.2 Printing the performance estimates of different models and identifying the best one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ccb77f3b",
   "metadata": {
    "id": "ccb77f3b"
   },
   "outputs": [],
   "source": [
    "# Defining code name of models\n",
    "\n",
    "def print_perf_esimates_return_best_model(error):\n",
    "\n",
    "    model_names = {}\n",
    "\n",
    "    model_names['AA'] = 'AutoARIMA'\n",
    "\n",
    "    model_names['PH'] = 'Prophet'\n",
    "\n",
    "    model_names['RF'] = 'RandomForest'\n",
    "\n",
    "    model_names['LG'] = 'LightGBMModel'\n",
    "\n",
    "    model_names['RNN'] = 'RNN (GRU)'\n",
    "\n",
    "    model_names['BRNN'] = 'BRNN (GRU)'\n",
    "    \n",
    "    best_model_code = error.idxmin()\n",
    "\n",
    "    error.index = error.index.map(lambda x: model_names[x])\n",
    "\n",
    "    # Printing different models performance estimates with default settings\n",
    "\n",
    "    print(\"Forecast errors (MAPE) of different models!!\")\n",
    "\n",
    "    print()\n",
    "\n",
    "    print(error.sort_values())\n",
    "\n",
    "    print()\n",
    "\n",
    "    print(f\"Best model is {model_names[best_model_code]} with MAPE of : {error.min():3.2f}\")\n",
    "\n",
    "    print()\n",
    "\n",
    "    return model_names[best_model_code]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79dc6fbd",
   "metadata": {
    "id": "79dc6fbd",
    "outputId": "cd340df2-b257-4f1d-9fd0-fdada3936045"
   },
   "source": [
    "## 4.3 Hyper-parameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "99b224f6",
   "metadata": {
    "id": "99b224f6"
   },
   "outputs": [],
   "source": [
    "def max_n_iter(param_grid):\n",
    "    \n",
    "    max_count =1\n",
    "    \n",
    "    for val in param_grid.values():\n",
    "\n",
    "        max_count = max_count * len(val)\n",
    "        \n",
    "    return max_count\n",
    "\n",
    "def hyp_tuning_forecast(bst_mdl_code,train_transformed,\n",
    "                        validation_transformed,full_series_transformed,\n",
    "                        trnsfm,covariates,n_jobs,n_iter_RF,\n",
    "                       n_iter_LG, n_iter_PH,n_iter_RNN,n_iter_BRNN):\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    Function performs hyper-parameter tuning for the model indicated through model code\n",
    "    \n",
    "    Input: \n",
    "    \n",
    "    mdl_code: Model code(Str)\n",
    "        \n",
    "    train_transformed - train part of target series transformed or normalized(TimeSeries object) \n",
    "    \n",
    "    validation_transformed - Validation part of target series transformed or normalized(TimeSeries object)\n",
    "    \n",
    "    validation - Validation part of target series(TimeSeries object)\n",
    "    \n",
    "    full_series_transformed - target series transformed or normalized(TimeSeries object)\n",
    "    \n",
    "    trnsfm - Transform object\n",
    "    \n",
    "    covariates - year, month covariates (normalized) stacked together(TimeSeries object)\n",
    "    \n",
    "    n_jobs: number of cores to use (int)\n",
    "    \n",
    "    Output: \n",
    "    \n",
    "    forecast - 12 time steps ahead forecast (TimeSeries object)\n",
    "        \n",
    "    \"\"\"\n",
    "    print()\n",
    "    \n",
    "    \n",
    "    if bst_mdl_code == 'RF':\n",
    "                \n",
    "        if len(full_series_transformed) >= 48:\n",
    "            \n",
    "            \n",
    "            param_grid = dict(lags=[int(x) for x in np.arange(6,25,1)],\n",
    "                      lags_future_covariates=[[0,]],\n",
    "                      lags_past_covariates=[[-6,-5,-4,-3,-2,-1,],],\n",
    "                      n_estimators=[int(x) for x in np.arange(20,151,10)],\n",
    "                      max_samples=[float(x) for x in np.round(np.arange(0.2,0.91,0.1),2)])\n",
    "            \n",
    "            max_iter = max_n_iter(param_grid)\n",
    "            \n",
    "            \n",
    "            best_model,best_hyp_params,val_err = RandomForest.gridsearch(parameters=param_grid,\n",
    "                                                                      series=train_transformed.astype('float32'),\n",
    "                                                                      future_covariates=covariates.astype('float32'),\n",
    "                                                                      past_covariates=covariates.astype('float32'),\n",
    "                                                                      val_series=validation_transformed.astype('float32'),\n",
    "                                                                      metric=mse,n_jobs=n_jobs,\n",
    "                                                                      n_random_samples=n_iter_RF,\n",
    "                                                                      verbose=2)\n",
    "\n",
    "            # Getting forecast\n",
    "\n",
    "            best_model.fit(full_series_transformed,future_covariates=covariates,past_covariates=covariates)\n",
    "        \n",
    "            \n",
    "        else:\n",
    "            \n",
    "            param_grid = dict(lags=[int(x) for x in np.arange(3,10,1)],\n",
    "                      lags_future_covariates=[[0,]],\n",
    "                      lags_past_covariates=[[-6,-5,-4,-3,-2,-1,],],\n",
    "                      n_estimators=[int(x) for x in np.arange(20,61,5)],\n",
    "                      max_samples=[float(x) for x in np.round(np.arange(0.2,0.91,0.1),2)])\n",
    "            \n",
    "            max_iter = max_n_iter(param_grid)\n",
    "            \n",
    "            \n",
    "            best_model,best_hyp_params,val_err = RandomForest.gridsearch(parameters=param_grid,\n",
    "                                                                      series=train_transformed.astype('float32'),\n",
    "                                                                      future_covariates=covariates.astype('float32'),\n",
    "                                                                      past_covariates=covariates.astype('float32'),\n",
    "                                                                      val_series=validation_transformed.astype('float32'),\n",
    "                                                                      metric=mse,n_jobs=n_jobs,\n",
    "                                                                      n_random_samples=n_iter_RF,\n",
    "                                                                      verbose=2)\n",
    "\n",
    "            # Getting forecast\n",
    "\n",
    "            best_model.fit(full_series_transformed,future_covariates=covariates,past_covariates=covariates)\n",
    "        \n",
    "\n",
    "        forecast = best_model.predict(n=12,future_covariates=covariates,past_covariates=covariates)\n",
    "        \n",
    "        clear_output()\n",
    "        \n",
    "        #print(best_hyp_params)\n",
    "        \n",
    "        #print(f\"Best performing model: RandomForest\")\n",
    "\n",
    "    elif bst_mdl_code == 'LG':\n",
    "        \n",
    "        if len(full_series_transformed) >= 48:\n",
    "            \n",
    "            \n",
    "            param_grid = {'lags':[int(x) for x in np.arange(6,25,1)],\n",
    "                          'lags_future_covariates':[[0,]],\n",
    "                          'lags_past_covariates':[[-6,-5,-4,-3,-2,-1,],],\n",
    "                          'learning_rate':[0.01,0.04,0.08,0.2,0.5,0.6],\n",
    "                          'max_depth':[int(x) for x in np.arange(2,10,1)],\n",
    "                          'n_estimators':[int(x) for x in np.arange(20,200,10)],\n",
    "                          'subsample':[float(np.round(x,2)) for x in np.arange(0.2,0.91,0.2)],\n",
    "                          'colsample_bytree':[float(np.round(x,2)) for x in np.arange(0.2,0.91,0.2)],\n",
    "                          'reg_lambda':[0.3,0.5,0.8,1],'objective':['regression',],'num_leaves':[35,]}\n",
    "            \n",
    "            max_iter = max_n_iter(param_grid)\n",
    "            \n",
    "            \n",
    "            best_model,best_hyp_params,val_err = LightGBMModel.gridsearch(parameters=param_grid,\n",
    "                                                                      series=train_transformed,\n",
    "                                                                      future_covariates=covariates,\n",
    "                                                                      past_covariates=covariates,\n",
    "                                                                      val_series=validation_transformed,\n",
    "                                                                      metric=mse,n_jobs=n_jobs,\n",
    "                                                                          n_random_samples=n_iter_LG,\n",
    "                                                                         verbose=2)\n",
    "\n",
    "            # Getting forecast\n",
    "\n",
    "            best_model.fit(full_series_transformed,future_covariates=covariates,past_covariates=covariates)\n",
    "        \n",
    "        else:\n",
    "            \n",
    "            param_grid = {'lags':[int(x) for x in np.arange(3,10,1)],\n",
    "                          'lags_future_covariates':[[0,]],\n",
    "                          'lags_past_covariates':[[-6,-5,-4,-3,-2,-1,],],\n",
    "                          'learning_rate':[0.01,0.04,0.08,0.2,0.5,0.6],\n",
    "                          'max_depth':[int(x) for x in np.arange(2,5,1)],\n",
    "                          'n_estimators':[int(x) for x in np.arange(20,91,10)],\n",
    "                          'subsample':[float(np.round(x,2)) for x in np.arange(0.2,0.91,0.2)],\n",
    "                          'colsample_bytree':[float(np.round(x,2)) for x in np.arange(0.2,0.91,0.2)],\n",
    "                          'reg_lambda':[0.3,0.5,0.8,1],'objective':['regression',],'num_leaves':[16,]}\n",
    "            \n",
    "            max_iter = max_n_iter(param_grid)\n",
    "            \n",
    "            \n",
    "            best_model,best_hyp_params,val_err = LightGBMModel.gridsearch(parameters=param_grid,\n",
    "                                                                      series=train_transformed,\n",
    "                                                                      future_covariates=covariates,\n",
    "                                                                      past_covariates=covariates,\n",
    "                                                                      val_series=validation_transformed,\n",
    "                                                                      metric=mse,n_jobs=n_jobs,\n",
    "                                                                          n_random_samples=n_iter_LG,\n",
    "                                                                         verbose=2)\n",
    "\n",
    "            # Getting forecast\n",
    "\n",
    "            best_model.fit(full_series_transformed,future_covariates=covariates,past_covariates=covariates)\n",
    "        \n",
    "\n",
    "        forecast = best_model.predict(n=12,future_covariates=covariates,past_covariates=covariates)\n",
    "        \n",
    "        clear_output()\n",
    "        \n",
    "        #print(best_hyp_params)\n",
    "        \n",
    "        #print(f\"Best performing model: LightGBMModel\")\n",
    "\n",
    "    elif bst_mdl_code == 'PH':\n",
    "        \n",
    "        \n",
    "        param_grid = {\n",
    "            'changepoint_prior_scale': [0.001, 0.003,0.01,0.03, 0.1,0.3, 0.9],\n",
    "            'seasonality_prior_scale': [0.01, 0.03, 0.1, 0.3, 1.0, 3, 10],\n",
    "            'seasonality_mode': ['additive','multiplicative'],\n",
    "            'changepoint_range': np.round(np.arange(0.8,0.96,0.05),2)}\n",
    "        \n",
    "        max_iter = max_n_iter(param_grid)\n",
    "        \n",
    "        \n",
    "        best_model,best_hyp_params,val_err = Prophet.gridsearch(parameters=param_grid,\n",
    "                                                                  series=train_transformed,\n",
    "                                                                  future_covariates=covariates,\n",
    "                                                                  val_series=validation_transformed,\n",
    "                                                                  metric=mse,n_jobs=n_jobs,\n",
    "                                                                n_random_samples=n_iter_PH,\n",
    "                                                               verbose=2)\n",
    "        \n",
    "        # Getting forecast\n",
    "                \n",
    "        best_model.fit(full_series_transformed,future_covariates=covariates)\n",
    "        \n",
    "        forecast = best_model.predict(n=12,future_covariates=covariates)\n",
    "        \n",
    "        clear_output()\n",
    "        \n",
    "        #print(best_hyp_params)\n",
    "        \n",
    "        #print(f\"Best performing model: Prophet\")\n",
    "    \n",
    "    elif bst_mdl_code == 'AA':\n",
    "        \n",
    "        # AutoARIMA model automatically performs hyp-tuning\n",
    "        # under the hood with ARIMA model hence no hyp-tuning for this model\n",
    "        \n",
    "        # Getting validation error\n",
    "        \n",
    "        try:\n",
    "                        \n",
    "            best_model = AutoARIMA(m=12,maxiter=100)\n",
    "        \n",
    "            best_model.fit(full_series_transformed,future_covariates=covariates)\n",
    "        \n",
    "            forecast = best_model.predict(n=12,future_covariates=covariates)\n",
    "\n",
    "        \n",
    "        except:\n",
    "            \n",
    "                        \n",
    "            best_model = AutoARIMA(maxiter=100)\n",
    "        \n",
    "            best_model.fit(full_series_transformed,future_covariates=covariates)\n",
    "        \n",
    "            forecast = best_model.predict(n=12,future_covariates=covariates)\n",
    "\n",
    "        clear_output()\n",
    "        \n",
    "        #print(f\"Best performing model: AutoARIMA\" )\n",
    "    \n",
    "    # NOTE: Distribution of hyper-parameter tuning process is not working for RNN, BRNN models\n",
    "    #       hence just using one cpu.\n",
    "    \n",
    "    elif bst_mdl_code == 'RNN':\n",
    "\n",
    "        param_grid = {'model':[\"GRU\",],\n",
    "              'input_chunk_length':[int(x) for x in np.arange(6,25,1)],\n",
    "              'n_rnn_layers':[int(x) for x in np.arange(1,4,1)],\n",
    "              'hidden_dim':[int(x) for x in np.arange(1,6,1)],\n",
    "              'n_epochs':[100,],\n",
    "              }\n",
    "\n",
    "\n",
    "        max_iter = max_n_iter(param_grid)\n",
    "            \n",
    "        \n",
    "        best_model,best_hyp_params,val_err = RNNModel.gridsearch(parameters=param_grid,\n",
    "                                                                  series=train_transformed,\n",
    "                                                                  val_series=validation_transformed,\n",
    "                                                                  future_covariates=covariates,\n",
    "                                                                  metric=mse,n_jobs=1,\n",
    "                                                                 n_random_samples=n_iter_RNN,\n",
    "                                                                verbose=2)\n",
    "        \n",
    "\n",
    "\n",
    "        best_model.force_reset = True\n",
    "\n",
    "        # Getting forecast\n",
    "\n",
    "        best_model.fit(full_series_transformed,future_covariates=covariates)\n",
    "\n",
    "        forecast = best_model.predict(n=12,future_covariates=covariates)\n",
    "\n",
    "        clear_output()\n",
    "\n",
    "        #print(best_hyp_params)\n",
    "\n",
    "        #print(f\"Best performing model: RNN (GRU)\")\n",
    "\n",
    "\n",
    "    elif bst_mdl_code == 'BRNN':\n",
    "        \n",
    "        param_grid = {'model':[\"GRU\",],\n",
    "              'input_chunk_length':[int(x) for x in np.arange(6,25,1)],\n",
    "              'output_chunk_length':[1,],\n",
    "              'n_rnn_layers':[int(x) for x in np.arange(1,4,1)],\n",
    "              'hidden_size':[int(x) for x in np.arange(1,6,1)],\n",
    "              'n_epochs':[100,],\n",
    "              }\n",
    "\n",
    "        max_iter = max_n_iter(param_grid)\n",
    "            \n",
    "        best_model,best_hyp_params,val_err = BlockRNNModel.gridsearch(parameters=param_grid,\n",
    "                                                                  series=train_transformed,\n",
    "                                                                  val_series=validation_transformed,\n",
    "                                                                  past_covariates=covariates,\n",
    "                                                                  metric=mse,n_jobs=1,\n",
    "                                                                      n_random_samples=n_iter_BRNN,\n",
    "                                                                     verbose=2)\n",
    "\n",
    "        best_model.force_reset = True\n",
    "\n",
    "        # Getting forecast\n",
    "\n",
    "        best_model.fit(full_series_transformed,past_covariates=covariates)\n",
    "\n",
    "        forecast = best_model.predict(n=12,past_covariates=covariates)\n",
    "\n",
    "        clear_output()\n",
    "\n",
    "        #print(best_hyp_params)\n",
    "\n",
    "        #print(f\"Best performing model: BlockRNN (GRU)\")\n",
    "\n",
    "    \n",
    "    else:\n",
    "        \n",
    "        print(\"Provided model code is invalid!!\")\n",
    "        \n",
    "    \n",
    "    return trnsfm.inverse_transform(forecast)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "945a81f8",
   "metadata": {
    "id": "945a81f8"
   },
   "source": [
    "## 4.4 Main function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "139eedfd",
   "metadata": {
    "id": "139eedfd"
   },
   "outputs": [],
   "source": [
    "def main_function_3_FcstDefault_HypTuning(components,n_jobs,n_iter_RF,\n",
    "                       n_iter_LG, n_iter_PH,n_iter_RNN,n_iter_BRNN,show_plots):\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    Function performs three tasks\n",
    "    \n",
    "    1. \n",
    "    \n",
    "    Input: Forecast components produced by main_function_2.. (tuple)\n",
    "    \n",
    "    n_jobs: number of cpus to use (int)\n",
    "    \n",
    "    n_iter_RF: number of iteration of random foreast model (int)\n",
    "    \n",
    "    n_iter_LG: number of iterations for LightGBMModel model (int)\n",
    "    \n",
    "    n_iter_PH: number of iteration of Prophet model (int)\n",
    "    \n",
    "    n_iter_RNN: number of iteration of RNN model (int)\n",
    "    \n",
    "    n_iter_BRNN: number of iteration of BRNN model (int)\n",
    "    \n",
    "    show_plots: default (Boolean)\n",
    "    \n",
    "    Output: \n",
    "\n",
    "    forecast - (TimeSeries object) \n",
    "    climatology - (pandas DataFrame object) \n",
    "    trsfm - (darts Scaler transform object) \n",
    "    full_series - (TimeSeries object) \n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    (series_monthly_mean, climatology, trnsfm, validation,train_transformed, validation_transformed, full_series_transformed, covariates) = components\n",
    "    \n",
    "    \n",
    "    error, fig = get_forecast_perf_estimates(train_transformed,validation_transformed,\n",
    "                                validation,trnsfm,covariates,\n",
    "                                show_plots=True)\n",
    "    \n",
    "    clear_output()\n",
    "    \n",
    "    error = error.sort_values()\n",
    "    \n",
    "    best_model_code = error.index[0]\n",
    "    \n",
    "    best_error = error.values[0]\n",
    "    \n",
    "    next_best_model_code = error.index[1] \n",
    "    \n",
    "    next_best_error = error.values[1]\n",
    "    \n",
    "    if (best_model_code == 'LG') and (np.abs(best_error-next_best_error) <= 2):\n",
    "            \n",
    "        best_model_code = next_best_model_code\n",
    "        \n",
    "        going_with_next_best = True\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        going_with_next_best = False\n",
    "        \n",
    "        \n",
    "    forecast = hyp_tuning_forecast(best_model_code,train_transformed,\n",
    "                        validation_transformed,full_series_transformed,\n",
    "                        trnsfm,covariates,n_jobs,n_iter_RF,\n",
    "                       n_iter_LG, n_iter_PH,n_iter_RNN,n_iter_BRNN)\n",
    "    \n",
    "    \n",
    "    best_model_name = print_perf_esimates_return_best_model(error)     \n",
    "    \n",
    "    if going_with_next_best:\n",
    "        \n",
    "        print('Hyper-parametering tuning for RandomForest model and obtaining forecast')\n",
    "        \n",
    "        print()\n",
    "        \n",
    "        print(\"From error (MAPE) values, LightGBMModel is observed to be best but with < 5% difference from the next best model\")\n",
    "        \n",
    "        print()\n",
    "        \n",
    "        print('LightGBMModel is observed to produce constant value forecasts when provided data is < 5 years, which is not desirable.', end=' ')\n",
    "        \n",
    "        print('Hence forecast is produced with next best model')\n",
    "        \n",
    "        print()\n",
    "    \n",
    "    else:\n",
    "\n",
    "        print(f'Hyper-parametering tuning for {best_model_name} model and obtaining forecast')\n",
    "        \n",
    "        print()\n",
    "    \n",
    "\n",
    "    print(\"Plots of different model forecasts in the validation period with performance estimates\")\n",
    "    \n",
    "    print()\n",
    "    \n",
    "    if fig is not None:\n",
    "        \n",
    "        fig\n",
    "        \n",
    "    full_series = trnsfm.inverse_transform(full_series_transformed)\n",
    "    \n",
    "    return forecast, climatology, full_series\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f19dfe5c",
   "metadata": {
    "id": "f19dfe5c"
   },
   "source": [
    "<!--  -->"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "228977cd",
   "metadata": {
    "id": "228977cd"
   },
   "source": [
    "# 5. Computing forecast deviation from climatology, plotting results and saving"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbadfbf5",
   "metadata": {
    "id": "dbadfbf5"
   },
   "source": [
    "<!--  -->"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "628f78df",
   "metadata": {
    "id": "628f78df"
   },
   "source": [
    "## 5.1 Modifying forecast to readable format and calculating its deviation from climatology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dbe80a1e",
   "metadata": {
    "id": "dbe80a1e"
   },
   "outputs": [],
   "source": [
    "def compute_forecast_results(forecast, climatology):\n",
    "    \n",
    "    \"\"\"\n",
    "    Function changes the format of forecast provided to simple tabular format, adds climatology\n",
    "    \n",
    "    values for the corresponding months and also computes forecast deviation from the climatology in percentage(%)\n",
    "    \n",
    "    Input:\n",
    "    \n",
    "    foreacast: forecast values (TimeSeries object)\n",
    "    \n",
    "    climatology: climatology (pd.DataFrame object)\n",
    "    \n",
    "    Output:\n",
    "    \n",
    "    result (pd.DataFrame object)\n",
    "    \n",
    "    \"\"\"\n",
    "    forecast_df = forecast.pd_dataframe()\n",
    "\n",
    "    forecast_df.columns = ['forecast']\n",
    "\n",
    "    forecast_df = forecast_df.reset_index()\n",
    "\n",
    "    forecast_df.index = forecast_df['DATE'].dt.month\n",
    "\n",
    "    forecast_df.index.name = 'month'\n",
    "\n",
    "    result = pd.merge(forecast_df, climatology,on='month')\n",
    "\n",
    "    result.set_index('DATE',inplace=True)\n",
    "\n",
    "    result.index.name = 'month'\n",
    "\n",
    "    result['deviation in %'] = ((result['forecast'] - result['climatology'])/result['climatology']) * 100\n",
    "    \n",
    "    result.loc[result['forecast'] < 0,'forecast'] = 0\n",
    "    \n",
    "    return result\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f702a4f9",
   "metadata": {
    "id": "f702a4f9"
   },
   "source": [
    "## 5.2 Ploting forecast and climatology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d1beff41",
   "metadata": {
    "id": "d1beff41"
   },
   "outputs": [],
   "source": [
    "def plot_result(result,full_series,save_fig=True):\n",
    "    \n",
    "    \"\"\"\n",
    "    Plots the results provided.\n",
    "    \n",
    "    Input:\n",
    "    \n",
    "    result: output of compute_forecast_results function (pandas DataFrame object)\n",
    "    \n",
    "    full_series: (Timeseries object)\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    forecast = TimeSeries.from_series(result['forecast'])\n",
    "\n",
    "    climatology = TimeSeries.from_series(result['climatology'])\n",
    "    \n",
    "    fig,ax = plt.subplots(1,1,figsize=(20,16))\n",
    "    \n",
    "    full_series.plot(label='Time series',color='k',lw=5)\n",
    "    \n",
    "    forecast.plot(label='Forecast',color='b',lw=5)\n",
    "\n",
    "    climatology.plot(label='Climatology',color='g',lw=5)\n",
    "    \n",
    "    ax.axvline(x=result.index[0],color='grey',ls='--',lw=5)\n",
    "    \n",
    "    ax.tick_params(size=10,labelsize=20)\n",
    "    \n",
    "    ax.legend(fontsize=24)\n",
    "\n",
    "    ax.set_xlabel('Month',fontsize=24)\n",
    "    \n",
    "    ax.set_ylabel('Wind speed (m/s)',fontsize=24)\n",
    "    \n",
    "    if save_fig:\n",
    "        \n",
    "        plt.savefig(\"Forecast_plot.png\",dpi=300,facecolor='w')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff8f8929",
   "metadata": {
    "id": "ff8f8929"
   },
   "source": [
    "## 5.3 Main function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "98d4a07c",
   "metadata": {
    "id": "98d4a07c"
   },
   "outputs": [],
   "source": [
    "def main_function_4_ploting_fcst_clim_save_data(forecast, climatology, full_series, save_figure=True):\n",
    "    \n",
    "    \"\"\"\n",
    "    Function provides forecast deviation from climatology, plots and saves the data in csv file format\n",
    "    \n",
    "    Input:\n",
    "        \n",
    "    foreacast: forecast values (TimeSeries object)\n",
    "    \n",
    "    climatology: climatology (pd.DataFrame object)\n",
    "\n",
    "    full_series: (Timeseries object)\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    # Computing forecast results, climatology and forecast deviation from climatology \n",
    "\n",
    "    results = compute_forecast_results(forecast, climatology)\n",
    "\n",
    "    # Plotting forecast results, by default save_fig is set to False in order to save it, change it to True \n",
    "\n",
    "    plot_result(result=results, full_series=full_series,save_fig=save_figure)\n",
    "\n",
    "    # Changing index format\n",
    "\n",
    "    results.index = results.index.strftime('%b-%Y')\n",
    "    \n",
    "    # Printing results\n",
    "    \n",
    "    print(results)\n",
    "    \n",
    "    # Saving results in csv file format\n",
    "    \n",
    "    results.to_csv('Forecast_results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "918398fd",
   "metadata": {
    "id": "918398fd"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2360a320",
   "metadata": {
    "id": "2360a320"
   },
   "source": [
    "# 6. Execution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5574cf6e",
   "metadata": {
    "id": "5574cf6e",
    "outputId": "5e3f7b50-f16f-4241-b8fd-26e9137bed84"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total cpus in the system are:8\n"
     ]
    }
   ],
   "source": [
    "print(f'Total cpus in the system are:{os.cpu_count() // 2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c144d9b0",
   "metadata": {
    "id": "c144d9b0"
   },
   "source": [
    "## Parameters which can be modified (advanced users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "792ad6fc",
   "metadata": {
    "id": "792ad6fc"
   },
   "outputs": [],
   "source": [
    "# Number of cpus to use for distributing the computation load \n",
    "\n",
    "# Default set to 1 but can be set to upto total number of cpus as mentioned by output message of above cell\n",
    "\n",
    "n_jobs = 8\n",
    "\n",
    "# Number of parameter combinations to tune the forecasting model\n",
    "\n",
    "# In simple terms higher the number provided better could be the model fit.\n",
    "\n",
    "# Recommended range is next to default value in commented part\n",
    "\n",
    "# NOTE: Higher the number of n_iter, doesn't guarantee to give output.\n",
    "# Also increasing n_iter increases computation time \n",
    "\n",
    "n_iter_RF = 200 # (1-500) \n",
    "\n",
    "n_iter_LG = 800 # (1-2000)\n",
    "\n",
    "n_iter_PH = 100  # (1-392)\n",
    "\n",
    "n_iter_RNN = 50 # (1-285)\n",
    "\n",
    "n_iter_BRNN = 50# (1-285)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f483ceb0",
   "metadata": {
    "id": "f483ceb0"
   },
   "source": [
    "<!--  -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "CPrIoXnVjs68",
   "metadata": {
    "id": "CPrIoXnVjs68"
   },
   "outputs": [],
   "source": [
    "# Section 1: Importing required libraries (already done)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0b452137",
   "metadata": {
    "id": "0b452137"
   },
   "outputs": [],
   "source": [
    "# Section 2: Importing observation data and verifying required specifications\n",
    "\n",
    "series_daily_mean = main_function_1_import_verify_interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f6d866b7",
   "metadata": {
    "id": "f6d866b7",
    "outputId": "2218f11b-15aa-49c0-b10a-9725b18dba10"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 3: Computing required components for forecast process\n",
    "\n",
    "try:\n",
    "    \n",
    "    components = main_function_2_compute_fcst_req_cmpnts(series_daily_mean)\n",
    "    \n",
    "except AttributeError:\n",
    "    \n",
    "    print(\"Above section is not excecuted or an error occured in its execution\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d512c579",
   "metadata": {
    "id": "d512c579",
    "outputId": "e61eed96-0144-4f83-e09c-ba5cfb15eb77"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecast errors (MAPE) of different models!!\n",
      "\n",
      "LightGBMModel    50.036578\n",
      "AutoARIMA        63.917100\n",
      "Prophet          69.775101\n",
      "BRNN (GRU)       79.636663\n",
      "RNN (GRU)        80.009913\n",
      "RandomForest     82.021199\n",
      "dtype: float64\n",
      "\n",
      "Best model is LightGBMModel with MAPE of : 50.04\n",
      "\n",
      "Hyper-parametering tuning for LightGBMModel model and obtaining forecast\n",
      "\n",
      "Plots of different model forecasts in the validation period with performance estimates\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1728 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 4: Forecasting\n",
    "\n",
    "try:\n",
    "\n",
    "    forecast, climatology, full_series = main_function_3_FcstDefault_HypTuning(components,n_jobs,n_iter_RF,\n",
    "                           n_iter_LG, n_iter_PH,n_iter_RNN,n_iter_BRNN,show_plots=True)\n",
    "    \n",
    "except NameError:\n",
    "    \n",
    "    print(\"Above section is not excecuted or an error occured in its execution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "80d2c522",
   "metadata": {
    "id": "80d2c522",
    "outputId": "d5489907-198b-4515-84dc-17adadc11ce7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          forecast  climatology  deviation in %\n",
      "month                                          \n",
      "Mar-2022  0.890546     1.413891      -37.014516\n",
      "Apr-2022  0.890546     1.589632      -43.977835\n",
      "May-2022  0.890546     1.639998      -45.698323\n",
      "Jun-2022  0.890546     1.252242      -28.883879\n",
      "Jul-2022  0.890546     1.084481      -17.882736\n",
      "Aug-2022  0.890546     1.323864      -32.731266\n",
      "Sep-2022  0.890546     1.019457      -12.645000\n",
      "Oct-2022  0.890546     1.072035      -16.929362\n",
      "Nov-2022  0.890546     0.822682        8.249187\n",
      "Dec-2022  0.890546     0.650523       36.897021\n",
      "Jan-2023  0.890546     0.779632       14.226548\n",
      "Feb-2023  0.890546     1.014627      -12.229177\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 5: Computing forecast deviation from climatology, plotting results and saving\n",
    "\n",
    "try:\n",
    "    \n",
    "    main_function_4_ploting_fcst_clim_save_data(forecast, climatology, full_series, save_figure=True)\n",
    "    \n",
    "except NameError:\n",
    "    \n",
    "    print(\"Above section is not excecuted or an error occured in its execution\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6ea7bb5",
   "metadata": {
    "id": "b6ea7bb5"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff29b8a9",
   "metadata": {
    "id": "ff29b8a9"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0b5583be",
   "metadata": {
    "id": "0b5583be"
   },
   "source": [
    "<!--  -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d328018",
   "metadata": {
    "id": "1d328018"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "8d50eb05",
    "f1615f9d",
    "628f78df",
    "f702a4f9"
   ],
   "name": "Monthly_mean_forecast_generation_code.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}