Ensembles of Randomized NNs for Pattern-based Time Series Forecasting

TL;DR

This paper introduces an ensemble of randomized neural networks designed for pattern-based time series forecasting, effectively handling multiple seasonality and nonstationarity with superior accuracy and efficiency.

Contribution

It presents a novel ensemble approach with six diversity control strategies, improving forecasting accuracy over traditional models and state-of-the-art machine learning methods.

Findings

Outperforms statistical and machine learning models in accuracy

Handles multiple seasonality and nonstationarity effectively

Offers fast, simple, and high-precision forecasting

Abstract

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.