Data Matters

In 2003, Keogh, E., Chu, S., Hart, D., Pazzani and M. Segmenting had written Time Series: A Survey and Novel Approach. in Data Mining in Time Series Databases published by World Scientific Publishing Company. The article talks about how to convert time series data with high frequency noise into some linear regression segmentations. If the original time series data has N timestamps, the linear regression result has K segments(K is far smaller than N), the storage and transmission space required of the data would be minimized. The article summarized three typical method of segmenting time series: sliding window, top-down and bottom-up. Sliding window method is to consider the time series as a data stream, and segment the series from early time to later time data. Every time the method steps forward a timestamp, adds the current value to exist window, or closes the current window and establishes a new window for incoming data. The method could be used as online processing