A Time Series Forecasting Method

• Main Authors: Wang Zhao-Yu, Lin Yu-Chun, Lee Shie-Jue, Lai Chih-Chin
• Format: Article
• Language: English
• Published: EDP Sciences 2017-01-01
• Series: ITM Web of Conferences
• Online Access: https://doi.org/10.1051/itmconf/20171203008

This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The weighted self-constructing clustering processes all the data patterns incrementally. If a data pattern is not similar enough to an existing cluster, it forms a new cluster of its own. However, if a data pattern is similar enough to an existing cluster, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned for each cluster. Given a series of time-stamped data up to time t, we divide it into a set of training patterns. By using the weighted self-constructing clustering, the training patterns are grouped into a set of clusters. To estimate the value at time t + 1, we find the k nearest neighbors of the input pattern and use these k neighbors to decide the estimation. Experimental results are shown to demonstrate the effectiveness of the proposed approach.