Abstract
Similarity metrics measure distance to a compared time series. It allows for a classification and dependency search. These metrics are used for the selection of additional time series in forecasting, which involves advanced information on the target time series, known as exogenous data. Several studies demonstrate an accuracy gain by including such data that represents the environment for the target time series. Yet, robust significance analysis represents a key prerequisite for the correct context identification towards and accurate time series forecast.
For this reason, this article presents current similarity metrics and demonstrates significant aspects in aligning time series. The concept of dynamic comparison and its importance will be discussed as a basis for a robust perceptual significance analysis. By employing a pair of exemplary load and exogenous time series, alignment capabilities of promising distance metrics were tested, thus demonstrating gaps for an effective perceptual computing methodology. Finally, this paper examines prerequisites necessary for a new robust significance analysis methodology on three characteristic exogenous and target time-series combinations.
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Thanks to Ben Haymond for his help in the language editing of this abstract.
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Christen, R., Mazzola, L., Denzler, A., Portmann, E. (2022). Distance Metrics for Evaluating the Use of Exogenous Data in Load Forecasting. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1602. Springer, Cham. https://doi.org/10.1007/978-3-031-08974-9_37
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