A review on time series data mining

By Tak-Chung Fu. Engineering applications of artificial intelligence, 2011

1,509 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5c08ccba51c040a48907d3d9ab997c43bf17c43d

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Citations in the corpus

The Impact of Structured Event Embeddings on Scalable Stock Forecasting Models (2015, background): “Shallow and deep learning techniques have been used in stock price forecasting, such as artificial neural networks, support vector machines [10] and ensembles, since that discovering recurrent, interesting, and useful patterns in time series data is a non-trivial task [8].”
Plato (2020, positive): “Both T 1 and T 2 have 600 data points at 1-second resolution, are segmented by variable length segmentation methods and compressed by PLR (1-degree polynomial functions) [19] .”
Mining electronic health records: towards better research applications and clinical care (2012, methodology): “Health data that are recorded over short timescales (such as time series data of blood glucose levels) are sometimes collected in EHR systems; for these short-term data, various analysis methods exist 59 .”
A Survey of Sequential Pattern Mining (2017, background): “For more details about time-series transformations, the reader may refer to a survey of methods for time-series data mining [23].”
Social big data: Recent achievements and new challenges (2015, methodology): “Based on this representation, different data mining techniques can be applied such as pattern discovery, classification, rule discovery, and summarisation [83].”
A Bag-of-Features Framework to Classify Time Series (2013, background): “The random length subsequences can potentially detect patterns that appear with different lengths and be split across the time points [43].”
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].”
Multi-head CNN-RNN for multi-time series anomaly detection: An industrial case study (2019, background): “presented another survey focused on data mining techniques [2].”
Data Mining for the Internet of Things: Literature Review and Challenges (2015, background): “Commonly, time series task relies on 3 parts of components, including representation, similarity measures, and indexing (see Figure 5) [82, 83].”
TSclust: An R Package for Time Series Clustering (2014, background): “Fu (2011) provides a complete and comprehensive overview on recent time series data mining directions, including a range of key problems such as representation, indexing and segmentation of series, measures of dissimilarity, clustering procedures and visualization tools.”
Complex network approaches to nonlinear time series analysis (2019, methodology): “Note that the interesting pattern to be discovered here relate to rather general categories, including patterns that appear frequently versus such that occur rather surprisingly in the datasets [10,11].”
Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey (2013, neutral): “Many approaches for temporal data abstraction come from the field of data mining (see Fu [102] for a recent survey).”