ENCODE- Ensemble neural combination for optimal dimensionality encoding in time-series forecasting

Nowadays, predictive models based on a time series are widely used in many fields, from geology to healthcare and from traffic management to industrial production. One of the most important tasks in machine and deep learning is designing predictive algorithms that provide increasingly reliable and accurate forecasts from a time series. This work proposes a novel ensemble approach to producing predictions in a multivariate framework. Its main idea is to reduce data dimensionality through an encoding technique, with the aim to extract useful information via single predictive procedures and then to gather all the processed data through a combiner to give the final forecast. In our framework, the combiner is composed of a hybrid neural architecture: a Convolutional Neural Network to extract local patterns from the predictions and a Recurrent Neural Network to infer information about the temporal patterns of the time series. Furthermore, a fully connected network is adopted to merge these two components and to provide the prediction. Extensive experiments on different datasets, both public and private, related to different applications have been carried out. Comparisons of the errors with conventional methods and state-of-the-art strategies confirm both accuracy and robustness of the proposed ensemble. Finally, we also show a comparison in terms of computational time, both in the hyperparameter optimization and forecasting tasks.

Automated summary

This study proposes a novel ensemble approach for generating predictions in a multivariate framework. It reduces data dimensionality through an encoding technique, extracts useful information via single predictive procedures, and combines the processed data to produce the final forecast. Extensive experiments demonstrate the higher accuracy and robustness of the proposed ensemble compared to conventional methods and state-of-the-art strategies.