Time-series data mining

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A Review on Outlier/Anomaly Detection in Time Series Data (2020, background): “Alternatively, Galeano et al. (2006) suggest reducing the dimensionality with projection pursuit, which aims to find the best projections to identify outliers.”
Deep Learning for Time-Series Analysis (2017, methodology): “ssed in the literature by a range of dierent approches (for a recent review of the main techniques applied to perform tasks such as Classication, Segmentation, Anomaly Detection and Prediction, see [14]). This paper reviews some of the recently presented approaches to performing tasks related to Tim”
Spatio-Temporal Data Mining (2017, methodology): “A review of techniques for identifying such frequent patterns in time-series is presented in [Fu 2011] and [Esling and Agon 2012].”
Deep Learning for Time Series Anomaly Detection: A Survey (2022, neutral): “With more data available, algorithms becoming more efficient, and computing power increasing, time series analysis are increasingly being used to address business issues through forecasting, classification, and anomaly detection [59], [26].”