Machine learning tools to time series forecasting

K. Ramírez-Amaro; J. C. Chimal-Eguía

doi:10.1109/MICAI.2007.42

Machine learning tools to time series forecasting

K. Ramírez-Amaro, J. C. Chimal-Eguía

Centro de Investigación en Computación (CIC)

Research output: Contribution to conference › Paper

Abstract

In this paper a new input representation of the data of the time series and a new learning approach is presented. The input data representation is based on the information obtained by the division of image axis of the time series into boxes. Then, this new information is implemented in a new learning technique which through probabilistic mechanism this learning could be applied to the interesting forecasting problem. The results indicate that using the methodology proposed in this article it is possible to obtain forecasting results with good enough accuracy. © 2008 IEEE.

Original language	American English
Pages	91-101
Number of pages	80
DOIs	https://doi.org/10.1109/MICAI.2007.42
State	Published - 24 Dec 2008
Event	Proceedings - 2007 6th Mexican International Conference on Artificial Intelligence, Special Session, MICAI 2007 - Duration: 24 Dec 2008 → …

Conference

Conference	Proceedings - 2007 6th Mexican International Conference on Artificial Intelligence, Special Session, MICAI 2007
Period	24/12/08 → …

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10.1109/MICAI.2007.42

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AB - In this paper a new input representation of the data of the time series and a new learning approach is presented. The input data representation is based on the information obtained by the division of image axis of the time series into boxes. Then, this new information is implemented in a new learning technique which through probabilistic mechanism this learning could be applied to the interesting forecasting problem. The results indicate that using the methodology proposed in this article it is possible to obtain forecasting results with good enough accuracy. © 2008 IEEE.

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Machine learning tools to time series forecasting

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