Abstract
Time series classification has attracted increasing attention in machine learning and data mining. In the analysis of time series data, how to represent data is a critical step for the performance. Generally, we can regard each time stamp as a feature dimension for time series data instance. However, this näive representation might be not suitable for data analysis due to the over-fitting of data. To address this problem, we proposed a temporal bag-of-words representation for time series classification. A codebook is generated by the representative subsequences from the time series data. Consequently, we encode a time series data instance by the codebook, which describes different local patterns of time series data. In our experiments, we demonstrate that our proposed method can achieve better results by comparing with competitive methods.
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Gui, ZW., Yeh, YR. (2014). Time Series Classification with Temporal Bag-of-Words Model. In: Cheng, SM., Day, MY. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2014. Lecture Notes in Computer Science(), vol 8916. Springer, Cham. https://doi.org/10.1007/978-3-319-13987-6_14
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DOI: https://doi.org/10.1007/978-3-319-13987-6_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13986-9
Online ISBN: 978-3-319-13987-6
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