NDLI: Multi-horizon ternary time series forecasting

Content Provider	IEEE Xplore Digital Library
Author	Htike, Z.Z.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., IIUM, Kuala Lumpur, Malaysia (Htike, Z.Z.)
Abstract	Time series forecasting techniques have been widely applied in domains such as weather forecasting, electric power demand forecasting, earthquake forecasting, and financial market forecasting. Because of the fact that these time series are affected by a multitude of interrelating macroscopic and microscopic variables, the underlying models that generate these time series are nonlinear and extremely complex. Therefore, it is computationally infeasible to develop full-scale models with the present computing technology. Therefore, researchers have resorted to smaller-scale models that require frequent recalibration. Despite advances in forecasting technology over the past few decades, there have not been algorithms that can consistently produce accurate forecasts with statistical significance. This is mainly because state-of-the-art forecasting algorithms essentially perform single-horizon forecasts and produce continuous numbers as outputs. This paper proposes a novel multi-horizon ternary forecasting algorithm that forecasts whether a time series is heading for an uptrend or downtrend, or going sideways. The proposed system utilizes a cascade of support vector machines, each of which is trained to forecast a specific horizon. Individual forecasts of these support vector machines are combined to form an extrapolated time series. A higher level forecasting system then forward-runs the extrapolated time series and then forecasts the future trend of the input time series in accordance with some volatility measure. Experiments have been carried out on some datasets. Over these datasets, this system achieves accuracy rates well above the baseline accuracy rate, implying statistical significance. The experimental results demonstrate the efficacy of our framework.
Sponsorship	IEEE Poland Sect. CAS Chapt.
Starting Page	337
Ending Page	342
File Size	679823
Page Count	6
File Format	PDF
ISBN	9788362065172
ISSN	23260319
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-26
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Poznan Univ of Technology
Subject Keyword	Support vector machines Accuracy Computational modeling Time series analysis Weather forecasting Predictive models Forecasting Cascaded SVMs; time series forecasting; ternaryforecasting; multi-horizon forecasting
Content Type	Text
Resource Type	Article

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