Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality

This work contributes to the development of neural forecasting models with novel randomization-based learning methods. These methods improve the fitting abilities of the neural model, in comparison to the standard method, by generating network parameters in accordance with the data and target function features. A pattern-based representation of time series makes the proposed approach useful for forecasting time series with multiple seasonality. In the simulation study, we evaluate the performance of the proposed models and find that they can compete in terms of forecasting accuracy with fully-trained networks. Extremely fast and easy training, simple architecture, ease of implementation, high accuracy as well as dealing with nonstationarity and multiple seasonality in time series make the proposed model very attractive for a wide range of complex time series forecasting problems.

Automated summary

This study contributes to the development of neural forecasting models with novel randomization-based learning methods, which improve fitting abilities and allow for forecasting time series with multiple seasonality. The proposed models show promising performance in terms of forecasting accuracy, training speed, simplicity, and robustness in dealing with nonstationarity and multiple seasonality in time series.