Method and tool for generating hybrid time series forecasts using
an error remodeling approach. These forecasting approaches utilize a recursive
technique for modeling the linearity of the series using a linear method
(e.g., ARIMA, Theta, etc.) and then models (forecasts) the residuals of the linear forecaster
using non-linear neural networks (e.g., ANN, ARNN, etc.). The hybrid architectures comprise three steps:
firstly, the linear patterns of the series are forecasted which are followed by an error re-modeling step,
and finally, the forecasts from both the steps are combined to produce the final output. This method additionally
provides the confidence intervals as needed. Ten different models can be implemented using this package.
This package generates different types of hybrid error correction models for time series forecasting
based on the algorithms by Zhang. (2003), Chakraborty et al. (2019), Chakraborty et al. (2020),
Bhattacharyya et al. (2021), Chakraborty et al. (2022), and Bhattacharyya et al. (2022)