class_docstr: Class for detecting time series data during its import.
class_docstr_tok: ["Class","for","detecting","time","series","data","during","its","import","."]
docstr: Prepares MultiModal data for time series forecasting task in a form of dictionary
  
  :param dataframe: pandas DataFrame to process
  :param columns: column names, which should be used as features in forecasting
  
  :return multi_modal_ts_data: dictionary with numpy arrays
docstr_tok: ["Prepares","MultiModal","data","for","time","series","forecasting","task","in","a","form","of","dictionary",":","param","dataframe",":","pandas","DataFrame","to","process",":","param","columns",":","column","names",",","which","should","be","used","as","features","in","forecasting",":","return","multi_modal_ts_data",":","dictionary","with","numpy","arrays"]
code: def prepare_multimodal_data(
      dataframe: pd.DataFrame, columns: List[str]
  ) -> dict:
      """Prepares MultiModal data for time series forecasting task in a form of dictionary
  
      :param dataframe: pandas DataFrame to process
      :param columns: column names, which should be used as features in forecasting
  
      :return multi_modal_ts_data: dictionary with numpy arrays
      """
      multi_modal_ts_data = {}
      for column_name in columns:
          feature_ts = np.array(dataframe[column_name])
  
          # Will be the same
          multi_modal_ts_data.update({column_name: feature_ts})
  
      return multi_modal_ts_data
  
imports: from abc import abstractmethod
  from typing import List
  import numpy
  import pandas
  from fedot.core.constants import FRACTION_OF_UNIQUE_VALUES
  from fedot.utilities.custom_errors import AbstractMethodNotImplementError
label_desc: Use image node_id 1 for calling the TimeSeriesDataDetector obj's underlying member method code with example usage: obj.prepare_multimodal_data(dataframe, columns) and returns: multi_modal_ts_data