Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification

• URL: http://arxiv.org/abs/2303.17809v1
• Date: Fri, 31 Mar 2023 05:55:54 GMT
• Title: Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification
• Authors: Trent Henderson, Annie G. Bryant, Ben D. Fulcher
• Abstract summary: We evaluate the performance of an extremely simple classification approach. We find that a simple linear model based on the mean and standard deviation performs better at classifying individuals with schizophrenia.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The variety of complex algorithmic approaches for tackling time-series classification problems has grown considerably over the past decades, including the development of sophisticated but challenging-to-interpret deep-learning-based methods. But without comparison to simpler methods it can be difficult to determine when such complexity is required to obtain strong performance on a given problem. Here we evaluate the performance of an extremely simple classification approach -- a linear classifier in the space of two simple features that ignore the sequential ordering of the data: the mean and standard deviation of time-series values. Across a large repository of 128 univariate time-series classification problems, this simple distributional moment-based approach outperformed chance on 69 problems, and reached 100% accuracy on two problems. With a neuroimaging time-series case study, we find that a simple linear model based on the mean and standard deviation performs better at classifying individuals with schizophrenia than a model that additionally includes features of the time-series dynamics. Comparing the performance of simple distributional features of a time series provides important context for interpreting the performance of complex time-series classification models, which may not always be required to obtain high accuracy.

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