Machine Learning Tools to Time Series Forecasting
| Autor: | J. C. Chimal-Eguía, Karinne Ramirez-Amaro |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
business.industry
Computer science Active learning (machine learning) Probabilistic logic Multi-task learning Online machine learning Machine learning computer.software_genre External Data Representation Data structure Artificial intelligence Time series business Representation (mathematics) computer |
| Zdroj: | 2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI). |
| DOI: | 10.1109/micai.2007.42 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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