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Created ARIMA model #100

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Oct 13, 2024
Merged

Created ARIMA model #100

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40a39b4
Created arima model
DarshAgrawal14 Oct 8, 2024
f244cc7
added requirements.txt
DarshAgrawal14 Oct 11, 2024
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337 changes: 337 additions & 0 deletions ARIMA/ARIMA_V2.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Intial code\n",
"for reference purposes"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# import pandas as pd\n",
"# import numpy as np\n",
"# from sklearn.metrics import mean_squared_error\n",
"# from sklearn.preprocessing import StandardScaler\n",
"# from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
"# from statsmodels.tools.sm_exceptions import ConvergenceWarning\n",
"# import warnings\n",
"\n",
"# # Ignore convergence warnings\n",
"# warnings.simplefilter(\"ignore\")\n",
"\n",
"# # Load dataset with parsed dates\n",
"# data = pd.read_csv('../Data/SBI Train data.csv', parse_dates=['Date'], dayfirst=True)\n",
"\n",
"# # Set the index to the Date column\n",
"# data.set_index('Date', inplace=True)\n",
"# # data = data.asfreq('D')\n",
"# # Feature Engineering: Add day of week and month\n",
"# data['day_of_week'] = data.index.dayofweek\n",
"# data['month'] = data.index.month\n",
"\n",
"# # Add lagged value of the Close price and moving averages\n",
"# data['lagged_close'] = data['Close'].shift(1) \n",
"# data['moving_avg_3'] = data['Close'].rolling(window=3).mean()\n",
"# data['moving_avg_7'] = data['Close'].rolling(window=7).mean() # New: 7-day moving average for long-term trend\n",
"\n",
"# # Add Volume as a feature (scaling might help)\n",
"# data['volume'] = data['Volume']\n",
"\n",
"# # Drop rows with NaN values\n",
"# data.dropna(inplace=True)\n",
"\n",
"# # Standardize the features (important for scaling)\n",
"# scaler = StandardScaler()\n",
"# exog_features = ['day_of_week', 'month', 'lagged_close', 'moving_avg_3', 'moving_avg_7', 'volume']\n",
"# data[exog_features] = scaler.fit_transform(data[exog_features])\n",
"\n",
"# # Split the data into training and testing sets\n",
"# train_size = int(len(data) * 0.8)\n",
"# train, test = data.iloc[:train_size], data.iloc[train_size:]\n",
"\n",
"# # Tune SARIMAX hyperparameters (ARIMA order (p, d, q))\n",
"# order = (2, 1, 2) # Consider using AIC/BIC for finding optimal order\n",
"# seasonal_order = (1, 1, 1, 12) # Adding seasonality with monthly frequency\n",
"\n",
"# # Fit the SARIMAX model\n",
"# try:\n",
"# model = SARIMAX(train['Close'], \n",
"# exog=train[exog_features],\n",
"# order=order,\n",
"# seasonal_order=seasonal_order)\n",
"# model_fit = model.fit(disp=False)\n",
"# except ConvergenceWarning as e:\n",
"# print(f\"Convergence warning: {e}\")\n",
"# except Exception as e:\n",
"# print(f\"Error: {e}\")\n",
"\n",
"# # Forecasting\n",
"# forecast = model_fit.forecast(steps=len(test), exog=test[exog_features])\n",
"\n",
"# # Calculate RMSE for forecast\n",
"# rmse_arimax = np.sqrt(mean_squared_error(test['Close'], forecast))\n",
"# print(f\"Improved ARIMAX Model RMSE: {rmse_arimax}\")\n",
"\n",
"# test_prices = [i for i in test['Close']]\n",
"# # Check residuals diagnostics (optional)\n",
"# residuals = test_prices - forecast\n",
"# print(\"Mean of residuals:\", residuals.mean())\n",
"# print(\"Standard deviation of residuals:\", residuals.std())\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### immporting necessary libraries"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pickle\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.preprocessing import StandardScaler\n",
"from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
"from statsmodels.tools.sm_exceptions import ConvergenceWarning\n",
"import warnings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Udating features to dataset for proper time-series analysis"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# Ignore convergence warnings\n",
"warnings.simplefilter(\"ignore\", ConvergenceWarning)\n",
"\n",
"# Load training dataset with parsed dates\n",
"train_data = pd.read_csv('../Data/SBI Train data.csv', parse_dates=['Date'], dayfirst=True)\n",
"\n",
"# Set the index to the Date column\n",
"train_data.set_index('Date', inplace=True)\n",
"\n",
"# Feature Engineering: Add day of week and month\n",
"train_data['day_of_week'] = train_data.index.dayofweek\n",
"train_data['month'] = train_data.index.month\n",
"\n",
"# Add lagged value of the Close price and moving averages\n",
"train_data['lagged_close'] = train_data['Close'].shift(1)\n",
"train_data['moving_avg_3'] = train_data['Close'].rolling(window=3).mean()\n",
"train_data['moving_avg_7'] = train_data['Close'].rolling(window=7).mean()\n",
"\n",
"# Add Volume as a feature (scaling might help)\n",
"train_data['volume'] = train_data['Volume']\n",
"\n",
"# Drop rows with NaN values after applying the rolling window and lagging\n",
"train_data.dropna(inplace=True)\n",
"\n",
"# Standardize the features\n",
"scaler = StandardScaler()\n",
"exog_features = ['day_of_week', 'month', 'lagged_close', 'moving_avg_3', 'moving_avg_7', 'volume']\n",
"train_data[exog_features] = scaler.fit_transform(train_data[exog_features])\n",
"\n",
"# Split the data into training and testing sets\n",
"train_size = int(len(train_data) * 0.8)\n",
"train, validation = train_data.iloc[:train_size], train_data.iloc[train_size:]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training and savinng model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" self._init_dates(dates, freq)\n",
"C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" self._init_dates(dates, freq)\n",
"C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
" warn('Non-invertible starting MA parameters found.'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model and scaler saved successfully.\n"
]
}
],
"source": [
"# Train the SARIMAX model\n",
"order = (2, 1, 2)\n",
"seasonal_order = (1, 1, 1, 12)\n",
"\n",
"model = SARIMAX(train['Close'], exog=train[exog_features], order=order, seasonal_order=seasonal_order)\n",
"model_fit = model.fit(disp=False)\n",
"\n",
"# Save the model to a file using pickle\n",
"with open('sarimax_model.pkl', 'wb') as f:\n",
" pickle.dump(model_fit, f)\n",
"\n",
"# Optionally save the scaler as well\n",
"with open('scaler.pkl', 'wb') as f:\n",
" pickle.dump(scaler, f)\n",
"\n",
"print(\"Model and scaler saved successfully.\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Loading saved model"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Load the model and scaler from the files\n",
"with open('sarimax_model.pkl', 'rb') as f:\n",
" loaded_model = pickle.load(f)\n",
"\n",
"with open('scaler.pkl', 'rb') as f:\n",
" loaded_scaler = pickle.load(f)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Loading and processing Test data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Load the test dataset\n",
"test_data = pd.read_csv('../Data/SBI Test data.csv', parse_dates=['Date'], dayfirst=True)\n",
"\n",
"# Set the index to the Date column\n",
"test_data.set_index('Date', inplace=True)\n",
"\n",
"# Apply the same feature engineering on the test data\n",
"test_data['day_of_week'] = test_data.index.dayofweek\n",
"test_data['month'] = test_data.index.month\n",
"test_data['lagged_close'] = test_data['Close'].shift(1)\n",
"test_data['moving_avg_3'] = test_data['Close'].rolling(window=3).mean()\n",
"test_data['moving_avg_7'] = test_data['Close'].rolling(window=7).mean()\n",
"\n",
"# Add Volume as a feature\n",
"test_data['volume'] = test_data['Volume']\n",
"\n",
"# Drop rows with NaN values\n",
"test_data.dropna(inplace=True)\n",
"\n",
"# Standardize the features in the test dataset using the loaded scaler\n",
"test_data[exog_features] = loaded_scaler.transform(test_data[exog_features])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predicting share prices using model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
" return get_prediction_index(\n",
"C:\\Users\\agraw\\AppData\\Roaming\\Python\\Python311\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:837: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
" return get_prediction_index(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Data RMSE: 4.673693537736142\n",
"Mean of residuals: 0.311316834051805\n",
"Standard deviation of residuals: 4.664969245987366\n"
]
}
],
"source": [
"# Forecasting on the test data using the loaded model\n",
"forecast_test = loaded_model.forecast(steps=len(test_data), exog=test_data[exog_features])\n",
"\n",
"# Calculate RMSE for forecast\n",
"rmse_test = np.sqrt(mean_squared_error(test_data['Close'], forecast_test))\n",
"print(f\"Test Data RMSE: {rmse_test}\")\n",
"\n",
"# Check residuals diagnostics (optional)\n",
"test_prices = test_data['Close'].values\n",
"residuals_test = test_prices - forecast_test\n",
"print(\"Mean of residuals:\", residuals_test.mean())\n",
"print(\"Standard deviation of residuals:\", residuals_test.std())"
]
}
],
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
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},
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
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"nbformat_minor": 2
}
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