Abstract
Time series data generation is a standing problem in nearly every field, such as science, business, medicine, industry, or even entertainment. As a result, there is a growing demand for analysing this data efficiently for gauging out useful information. The time series data has intrinsic features like noise, multidimensional, and large volume. When we talk about data mining, it requires a wide spectrum searching for similar patterns, such as query by content, clustering, or classification. These data mining tasks can take great help from a good and robust time series representations. It helps in the reduction of dimensions and noise adaptation and also in achieving key aspect, effectiveness, and efficiency of data processing. This chapter aims to review the basic as well as recent approaches for representations along with dimensionality reduction for time series data.
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Sharma, A., Kumar, A., Pandey, A.K., Singh, R. (2020). Time Series Data Representation and Dimensionality Reduction Techniques. In: Johri, P., Verma, J., Paul, S. (eds) Applications of Machine Learning. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3357-0_18
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DOI: https://doi.org/10.1007/978-981-15-3357-0_18
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