State of the Art Machine Learning Techniques for Time Series Forecasting: A Survey
Time Series Forecasting is vital for wide range of domains such as financial market forecasting, earthquake forecasting, weather forecasting, electric power demand forecasting and etc. The past 25 years of time series forecasting research that has been reviewed in (Tinbergen Institute Discussion Pap...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/48055/1/ID_122.pdf http://irep.iium.edu.my/48055/ http://telsatech.org/ |
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| Summary: | Time Series Forecasting is vital for wide range of domains such as financial market forecasting, earthquake forecasting, weather forecasting, electric power demand forecasting and etc. The past 25 years of time series forecasting research that has been reviewed in (Tinbergen Institute Discussion Paper: International Journal of Forecasting) for the period of 1985 to 2005. Therefore, the purpose of my paper is continue to review the recent 10 years of different state of the machine learning techniques for time series forecasting . The main contribution of this paper is to supply researchers with a cohesive overview of state of the art machine learning techniques (during the period of 2005 to 2015) and to identify possible opportunities for future research. |
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