Abstract
Temporal data is common in data mining applications. Typically, this is a result of continuously occurring processes in which the data is collected by hardware or software monitoring devices. The diversity of domains is quite significant and extends from the medical to the financial domain.
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- 1.
The concept of “dimension” can be defined in two ways for time series data. Each behavioral attribute in a multivariate series can be viewed as a dimension. Alternatively, the different values in a univariate time series can be viewed as dimensions. The usage is often dependent on the semantics of the application at hand.
- 2.
The mean of the squares is always no less than the square of the mean for any set of numeric elements. The difference between the two is equal to the variance, which is always nonnegative.
- 3.
The tracking Exchange Traded Fund (ETF) SPY was used.
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Aggarwal, C. (2015). Mining Time Series Data. In: Data Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-14142-8_14
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DOI: https://doi.org/10.1007/978-3-319-14142-8_14
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-14142-8
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