Multi-horizon ternary time series forecasting
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 mic...
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2013
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| Online Access: | http://irep.iium.edu.my/34350/ http://irep.iium.edu.my/34350/ http://irep.iium.edu.my/34350/8/zawzawTOC.pdf http://irep.iium.edu.my/34350/9/zawzawCoverMulti_Horizon.pdf http://irep.iium.edu.my/34350/14/43048.pdf |
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iium-343502015-06-01T01:39:54Z http://irep.iium.edu.my/34350/ Multi-horizon ternary time series forecasting Htike@Muhammad Yusof, Zaw Zaw Q Science (General) 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. 2013-09 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/34350/8/zawzawTOC.pdf application/pdf en http://irep.iium.edu.my/34350/9/zawzawCoverMulti_Horizon.pdf application/pdf en http://irep.iium.edu.my/34350/14/43048.pdf Htike@Muhammad Yusof, Zaw Zaw (2013) Multi-horizon ternary time series forecasting. In: 17th Conference on Signal Processing Algorithms, Architectures, Arrangements, and Applications (SPA 2013, 26-28 Sep 2013, Poznań, POLAND . http://www.spaconference.org.pl |
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Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
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IIUM Repository |
| collection |
Online Access |
| language |
English English English |
| topic |
Q Science (General) |
| spellingShingle |
Q Science (General) Htike@Muhammad Yusof, Zaw Zaw Multi-horizon ternary time series forecasting |
| description |
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. |
| format |
Conference or Workshop Item |
| author |
Htike@Muhammad Yusof, Zaw Zaw |
| author_facet |
Htike@Muhammad Yusof, Zaw Zaw |
| author_sort |
Htike@Muhammad Yusof, Zaw Zaw |
| title |
Multi-horizon ternary time series forecasting |
| title_short |
Multi-horizon ternary time series forecasting |
| title_full |
Multi-horizon ternary time series forecasting |
| title_fullStr |
Multi-horizon ternary time series forecasting |
| title_full_unstemmed |
Multi-horizon ternary time series forecasting |
| title_sort |
multi-horizon ternary time series forecasting |
| publishDate |
2013 |
| url |
http://irep.iium.edu.my/34350/ http://irep.iium.edu.my/34350/ http://irep.iium.edu.my/34350/8/zawzawTOC.pdf http://irep.iium.edu.my/34350/9/zawzawCoverMulti_Horizon.pdf http://irep.iium.edu.my/34350/14/43048.pdf |
| first_indexed |
2023-09-18T20:49:31Z |
| last_indexed |
2023-09-18T20:49:31Z |
| _version_ |
1777409900337430528 |