Time Series Forecasting

LSTM for Time Series Forecasting

  • Univariate LSTM Models : one observation time-series data, predict the next value in the sequence
  • Multivariate LSTM Models : two or more observation time-series data, predict the next value in the sequence
    • Multiple Input Series : two or more parallel input time series and an output time series that is dependent on the input time series
    • Multiple Parallel Series : multiple parallel time series and a value must be predicted for each
  • Univariate Multi-Step LSTM Models : one observation time-series data, predict the multi step value in the sequence prediction.
  • Multivariate Multi-Step LSTM Models : two or more observation time-series data, predict the multi step value in the sequence prediction.
    • Multiple Input Multi-Step Output.
    • Multiple Parallel Input and Multi-Step Output.

Machine Learning for Multivariate Input

Statistical Method for Multivariate Input

Machine Learning for Univariate Input

Statistical Method for Univariate Input

Jupyter Notebook Examples

Univariate ARIMA

import statsmodels

Univariate LSTM

import keras

Multivariate VAR

(Note: VAR should only for Stationary process - Wikipedia)

Multivariate LSTM

Prophet from Facebook

Note on Multivariate and Univariate