Temporal Data Mining: an overview

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Time-series data mining (2012, background): “Among other common choice for symbolic representations, we may cite MC [Reinert et al. 2000], HMM with discrete output distributions [Law and Kwok 2000], and 12:20 P. Esling and C. Agon grammar-based models [Antunes and Oliveira 2001].”
Sequence learning using the adaptive suffix trie algorithm (2012, background): “Sequential information is also important in engineering in tasks such as log based monitoring, robot navigation and telecommunication control[3].”
Wavelet Regression Model as an Alternative to Neural Networks for River Stage Forecasting (2011, background): “Consequently, the time series becomes a more manageable object, which permits the definition of efficient similarity measures and an easier application to common data mining operations with these kinds of representations ( Antunes and Oliveira 2001 ).”
Time-series clustering - A decade review (2015, background): “In both k-Means and k-Medoids clustering algorithms, number of clusters, k, has to be pre-assigned, which is not available or feasible to be determined for many applications, so it is impractical in obtaining natural clustering results and is known as one of their drawbacks in static objects [21] an”