Model-free prognostication of non-linear time series
Fig 2
Validation of the machine learning for model-free prognostication.
The figure provides the comparative performance results for the 5 methods. The MCML method had the best performance of all forecasting methods and generated forecasting results with the lowest MAE, MAPE, and RMSE in each case. Top left) Comparisons of time-series forecasting using our method (maximum correlation machine learning (MCML) with the autoregressive integrated moving average (ARIMA), trigonometric seasonality, Box-Cox transformation, ARMA errors, trend, and seasonal components (TBATS), threshold autoregressive (TAR), Prophet forecasting model (PROPHET) in the Germany dataset (n = 819) based on the training set (n = 634) and the testing set (n = 185). Bottom left) Comparisons of our method (MCML) to the conventional time series methods for the Germany dataset. Top Right) Comparisons of time-series forecasting using our method (MCML) with the autoregressive integrated moving average (ARIMA), trigonometric seasonality, Box-Cox transformation, ARMA errors, trend, and seasonal components (TBATS), threshold autoregressive (TAR), Prophet forecasting model (PROPHET) in the Australia dataset (n = 819) based on the training set (n = 689) and the testing set (n = 130). Bottom right) Comparisons of our method (MCML) to canonical time series methods for the Australia dataset.