Multivariable time series forecasting using model fusion

The forecasting of time series provides great convenience in our daily life. Studies of time series forecasting have been used in many fields such as financial models, weather, and traffic patterns. In this paper, we propose a model fusion-based time series forecasting to improve the forecasting accuracy and efficiency. We propose a time series forecasting scheme based on a multivariate grey model and uses artificial fish swarm algorithm to optimize the settings. We then propose two fusion models with the grey model-based schemes on two different perspectives: data decomposition, and weighted summation. We conduct evaluations based on real data series and compared them with other forecasting models. Results show that our model can achieve good prediction accuracy and efficiency, which can be used for time series forecasting in different scenarios. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a model fusion-based time series forecasting method to improve the accuracy and efficiency of predictions using multivariate grey model and artificial fish swarm algorithm. Two fusion models based on data decomposition and weighted summation achieve good prediction results in different scenarios.