Ensembles of Randomized NNs for Pattern-based Time Series Forecasting
This work addresses forecasting challenges for time series with nonstationarity and multiple seasonality, offering a fast and accurate method, though it appears incremental in its approach.
The authors tackled time series forecasting with multiple seasonality by proposing an ensemble of randomized neural networks with pattern-based representation, achieving superior accuracy over statistical and state-of-the-art machine learning models in case studies on four real-world problems.
In this work, we propose an ensemble forecasting approach based on randomized neural networks. Improved randomized learning streamlines the fitting abilities of individual learners by generating network parameters in accordance with the data and target function features. A pattern-based representation of time series makes the proposed approach suitable for forecasting time series with multiple seasonality. We propose six strategies for controlling the diversity of ensemble members. Case studies conducted on four real-world forecasting problems verified the effectiveness and superior performance of the proposed ensemble forecasting approach. It outperformed statistical models as well as state-of-the-art machine learning models in terms of forecasting accuracy. The proposed approach has several advantages: fast and easy training, simple architecture, ease of implementation, high accuracy and the ability to deal with nonstationarity and multiple seasonality in time series.