Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality
This provides an efficient solution for complex time series forecasting problems, though it appears incremental as it builds on existing neural forecasting approaches.
The authors tackled the problem of forecasting time series with multiple seasonality by developing neural models using randomization-based learning methods, which achieved competitive accuracy compared to fully-trained networks while offering extremely fast and easy training.
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.