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

Nhat-Duc Hoang,Anh-Duc Pham,Minh-Tu Cao

doi:10.1155/2014/754809

Abstract

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(FLSVRTSP). 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, theFLSVRTSPincorporates 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 theFLSVRTSPhas 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.

Highlights

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
The daily water flow data set consists of 273 data cases of daily water discharge at Palo Verde outfall drain, from 1/1/2011 to 9/30/2011
This paper has presented a novel hybrid artificial intelligence (AI) model, named as the FLSVRTSP, to assist decision-makers in dealing with time series forecasting

Summary

Introduction

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. 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]. 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]. For each time series, determination of a suitable embedding dimension is of major concern [8, 9]. These challenges necessitate the development of advanced approaches

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Conclusion