Abstract
The exponentially weighted moving average (EWMA) is an important tool in time series analysis. So far the research on EWMA is typically limited to the real (vector) space \(\mathbb {R}^n\). In this work we present an extension of this concept to arbitrary spaces. It is based on an interpretation of EWMA as a special case of weighted mean computation. We develop three computation methods. In addition to the direct computation in the original space, we particularly study an approach to embedding the data items of a time series into vector space. The feasibility of our EWMA computation framework is exemplarily demonstrated on strings.
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Welsing, A., Nienkötter, A., Jiang, X. (2021). Exponential Weighted Moving Average of Time Series in Arbitrary Spaces with Application to Strings. In: Torsello, A., Rossi, L., Pelillo, M., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2021. Lecture Notes in Computer Science(), vol 12644. Springer, Cham. https://doi.org/10.1007/978-3-030-73973-7_5
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DOI: https://doi.org/10.1007/978-3-030-73973-7_5
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