A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence

This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction . The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR’s tuning parameters, the incorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that the has achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction. 1. Introduction Generally, time series forecasting involves the prediction of future values of data based on discovering the pattern in the historical data series and extrapolating that pattern into the future. Time series forecasting is a widely discussed issue and its applications appear in various fields of business and engineering [1]. The reason is that prediction of future events is crucial for many kinds of planning and decision-making processes. Applications regarding time series data can be easily found in the literature, such as wind energy forecasting [2], water resource management [3], traffic accident prediction [4], and cash flow forecasting in construction projects [5]. Hence, it is not surprising that time series analyses and predictions are on the rise among researchers. Notably, constructing a predictive model for time series forecasting is a challenging task. It is because real-world time series data are often characterized by nonlinearity, being nonstationary, and irregularity [6]. Random noise and effect of unidentified factors are the main causes that degrade the prediction accuracy. Moreover, in most cases, the underlying model that generates the series is unknown and the process of discovering such model is oftentimes hindered by the stochastic nature of the time-dependent data [7]. Particularly, for each time series, determination of a suitable embedding dimension is also of major concern [8, 9]. Therefore, these challenges necessitate the development of advanced approaches. Over the recent years, there has been increasing efforts dedicated in establishing AI