code: def _minmax_scaler_scale_timeseries_numpy(data, scaler):
      data_scale = np.zeros(data.shape)
      for i in range(data.shape[1]):
          value_min = scaler.min_[i]
          value_scale = scaler.scale_[i]
          data_scale[:, i] = data[:, i] * value_scale + value_min
      return data_scale
  
file_path: BigDL/python/chronos/src/bigdl/chronos/data/utils/scale.py
imports: import numpy
  import pandas
  from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler, RobustScaler
label_desc: Use image node_id 8 for calling a global function with example usage: _minmax_scaler_scale_timeseries_numpy(data, scaler) and returns: data_scale