As data mining methods deal with data without considering their temporal priority, temporal data mining tries to keep the temporal priority of the time stamp data, and extracts the rules based on them. The methods used to deal with temporal data, like statistic methods, require quite a great deal of knowledge to understand the results. However, the managers, requiring such kind of information, may not take the results, since they make no sense to them.
Temporal data mining uses methods that are more empirical, intuitive, and much natural for people to understand. As the purpose to make data more understandable, temporal data mining uses methods developed with artificial intelligence, such as heuristic methods, neural networks, fuzzy logic, etc. In researches on short-term time series forecasting, many methods have been developed sound and well, but in those on long-term forecasting, they still lack some explainable results from well understood and reasonable methods. In this thesis we focus on the improvement of the accuracy of traditional short-term forecasting methods with a novel time series modeling method, including the extraction of local patterns and certain transition rules. Further more, we also propose two difference strategies for long-term forecasting with different aspects considered.

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