Time Series Analysis. A time series is a collection of temporal data objects; the characteristics of time series data include large data size, high dimensionality, and updating continuously. Commonly, time series task relies on 3 parts of components, including representation, similarity measures, and indexing (see Figure 5) [82, 83]. (i) One of the major reasons for time series representation is to reduce the dimension, and it divides into three categories: model based representation, non-data-adaptive representation, and data adaptive representation. The model based representations want to find parameters of underlying model for a representation. Important research works include ARMA [84] and the time series bitmaps research [85]. In non-data-adaptive representations, the parameters of the transformation remain the same for every time series regardless of its nature, related research including DFT [86], wavelet functions related topic [87], and PAA [72]. In data, adaptive representations, the parameters of a transformation will change according to the data available and related works including representations version of DFT [88]/PAA [89] and indexable PLA [90]. 6 International Journal of Distributed Sensor Networks (ii) The similarity measure of time series analysis is typically carried out in an approximate manner; the research directions include subsequence matching [91] and full sequence matching [92]. (iii) The indexing of time series analysis is closely associated with representation and similarity measure part; the research topic includes SAMs (Spatial Access Methods) and TS-Tree [93]

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- What do distinguish Time Series Analysis from other data mining functionalities ?