A Hybrid Prediction Model Using Statistical Forecasters and Deep Neural Networks

The ability to accurately predict future time series behavior in multiple steps, known as multi-horizon forecasting, is a vital aspect in various industries, including retail sales, energy consumption, server load, healthcare, weather, and others. We have proposed, in this paper, the use of statistical forecasters as covariates in a Deep Neural Network (DNN) model and evaluated its impact on forecast metrics. Our analysis covered four diverse datasets: M5, Stallion, Stock Market, and Synthetic. The results demonstrated that the inclusion of statistical predictors in the DNN model led to varying degrees of improvement in forecast performance, depending on the dataset and the chosen evaluation metric. In general, our findings suggest that incorporating statistical prediction as a covariate can be a valuable approach to improving multi-horizon prediction, especially in scenarios with data scarcity and intermittency. The hybrid model achieved consistent improvements, particularly on Symmetric Mean Absolute Percentage Error (SMAPE) across datasets, with statistically significant gains on synthetic and stock market series. Specifically, SMAPE was reduced by approximately 33% on synthetic and stock market datasets, by 15–20% on Stallion, and by around 6% on M5. These results confirm that integrating statistical forecasts as covariates can substantially enhance predictive accuracy, especially for volatile or synthetic series.