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• W3021724670 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." @default.
• W3021724670 created "2020-05-13" @default.
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• W3021724670 date "2020-01-01" @default.
• W3021724670 modified "2023-10-16" @default.
• W3021724670 title "Time Series Data Representation and Dimensionality Reduction Techniques" @default.
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• W3021724670 doi "https://doi.org/10.1007/978-981-15-3357-0_18" @default.
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