fixes

docs/source/examples/data.rst

@@ -0,0 +1,121 @@
+Working with data
+==============================================
+
+
+This example explains how to load your data.
+
+Fedot provides specific interface for operation with data.
+Fedot uses it's own data object notation (InputData). It contains index,
+features and target for each sample. You can create it from file using ``InputData.from_csv()`` method.
+You need to provide ``Task`` object with type of task you want to solve.
+Here examples of for tabular data:
+
+.. code-block:: python
+
+
+    from fedot.core.data.data import InputData
+    data_path = 'path_to_data'
+
+    data = InputData.from_csv(data_path,
+    target_columns='target',
+    task=Task(TaskTypesEnum.classification)) # or regression
+
+.. note::
+
+    There are 3 possible values for TaskType:
+    * ``TaskTypesEnum.classification``
+    * ``TaskTypesEnum.regression``
+    * ``TaskTypesEnum.ts_forecasting``
+
+.. note::
+
+    You can provide several target columns (For regression task).Then Fedot will recognise it as multiregression task supported natively.
+
+You also can create ``InputData`` from pandas ``DataFrame``:
+
+.. code-block:: python
+
+
+    from fedot.core.data.data import InputData
+
+
+    data = InputData.from_dataframe(features_df,
+                                    target_df,
+                                    task=Task(TaskTypesEnum.classification)) # or regression
+
+or from numpy array:
+
+.. code-block:: python
+
+
+    from fedot.core.data.data import InputData
+
+
+    data = InputData.from_numpy(features_array,
+                                target_array,
+                                task=Task(TaskTypesEnum.classification)) # or regression
+
+After you can split data on train/test set:
+
+.. code-block:: python
+
+
+    train, test = train_test_data_setup(data)
+
+and pass it to the model:
+
+.. code-block:: python
+
+
+    model = Fedot(...)
+    model.fit(train)
+    model.predict(test)
+
+For time series forecasting problem there is a little bit different approach for data initialization.
+Firstly you need to create a ``Task`` object:
+
+.. code-block:: python
+
+
+     from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
+    # specify the task and the forecast length (required depth of forecast)
+    task = Task(TaskTypesEnum.ts_forecasting,
+                TsForecastingParams(forecast_length=your_forecast_length))
+
+
+
+After that you can use ``Input_data.from_csv_series``
+
+.. code-block:: python
+
+
+    train_input = InputData.from_csv_time_series(task=task,
+                                                 file_path='time_series.csv',
+                                                 delimiter=',',
+                                                 target_column='value')
+
+But you also can create ``InputData`` from numpy :
+
+.. code-block:: python
+
+
+    train_input = InputData.from_numpy_time_series(series,
+                                                   task=task)
+
+After you can split data on train/test set (test set will contain last N values of the series by default):
+
+.. code-block:: python
+
+
+    train, test = train_test_data_setup(data)
+
+and pass it to the model:
+
+.. code-block:: python
+
+
+    model = Fedot(...)
+    model.fit(train)
+    model.forecast()
+
+Thus, this example shows how to operate with data in Fedot.

docs/source/examples/index.rst

@@ -11,6 +11,8 @@ In this section you can find notebooks and useful pipeline structures for variou
 
    classification_example
    regression_example
+   ts_forecasting
+   data
    notebooks
    classification_pipelines
    regression_pipelines