期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2015
卷号：80
期号：2
出版社：Journal of Theoretical and Applied
摘要：Data segmentation is one of the primary tasks of time series mining. This task is often used to generate interesting subsequences from a large time series sequence. Segmentation is one of the essential components in extracting significant patterns of weather time series data, which may be useful in identifying the trend and changes in weather prediction. The task use interpolation to approximate the signal with a best-fitting series and return the last point of the segments as change point or as a sequence of time points as a window. Sliding window algorithm (SWA) is a well-known time series data segmentation method, in which a segment with an error threshold and fixed window size is created when the change point is reached. In actual data such as weather data, SWA is unsuitable because appropriate error threshold and change point are required to avoid information loss. In this paper, we propose an adaptive sliding window algorithm (ASWA) that categorizes weather time series data based on the change point information.
