Abstract
Time series data abound and analysis of such data is challenging and potentially rewarding. One example is financial time series analysis. In time series analysis there is the issue of time dependency, that is, the state in the nearer past is more relevant to the current state than that in the more distant past. In this paper we study this issue by introducing time weighting into similarity measures, as similarity is one of the key notions in time series analysis methods.
We consider the generic neighbourhood counting similarity as it can be specialised for various forms of data by defining the notion of neighbourhood in a way that satisfies different requirements. We do so with a view to capturing time weights in time series. This results in a novel time weighted similarity for time series. A formula is also discovered for the similarity so that it can be computed efficiently.
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Wang, H., Lin, Z. (2008). A Time Weighted Neighbourhood Counting Similarity for Time Series Analysis. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2008. Lecture Notes in Computer Science(), vol 5009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79721-0_78
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DOI: https://doi.org/10.1007/978-3-540-79721-0_78
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