code: def run_multi_ts_forecast(forecast_length, is_multi_ts):
      """
      Function for run experiment with use multi_ts data type (is_multi_ts=True) for train set extension
      Or run experiment on one time-series (is_multi_ts=False)
      """
      train_data, test_data, task = prepare_data(
          forecast_length, is_multi_ts
      )
      # init model for the time series forecasting
      init_pipeline = ts_complex_ridge_smoothing_pipeline()
      model = Fedot(
          problem="ts_forecasting",
          task_params=task.task_params,
          timeout=5,
          n_jobs=1,
          max_depth=5,
          num_of_generations=20,
          pop_size=15,
          max_arity=4,
          cv_folds=None,
          initial_assumption=init_pipeline,
      )
      # fit model
      pipeline = model.fit(train_data)
      pipeline.show()
  
      # use model to obtain forecast
      forecast = model.predict(test_data)
      target = np.ravel(test_data.target)
  
      # visualize results
      model.plot_prediction()
  
      print(f"MAE: {mean_absolute_error(target, forecast)}")
      print(f"RMSE: {mean_squared_error(target, forecast)}")
      print(f"MAPE: {mean_absolute_percentage_error(target, forecast)}")
  
      print(
          model.get_metrics(
              metric_names=["rmse", "mae", "mape"], target=target
          )
      )
  
file_path: FEDOT/cases/multi_ts_level_forecasting.py
imports: import os
  import numpy
  from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error
  from examples.simple.time_series_forecasting.ts_pipelines import ts_complex_ridge_smoothing_pipeline
  from fedot import Fedot
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.repository.tasks import TsForecastingParams, Task, TaskTypesEnum
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 2 for calling a global function with example usage: run_multi_ts_forecast(forecast_length, is_multi_ts) without return types