Anomaly detection in time series: a comprehensive evaluation
Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …
ranging from manufacturing processes over finance applications to health care monitoring …
Insights into LSTM fully convolutional networks for time series classification
Long short-term memory fully convolutional neural networks (LSTM-FCNs) and Attention
LSTM-FCN (ALSTM-FCN) have shown to achieve the state-of-the-art performance on the …
LSTM-FCN (ALSTM-FCN) have shown to achieve the state-of-the-art performance on the …
Survey of Time Series Data Generation in IoT
Clustering-based simultaneous forecasting of life expectancy time series through long-short term memory neural networks
In this paper, we apply a functional clustering method to the multivariate time series of life
expectancy at birth of the female populations collected in the Human Mortality Database. We …
expectancy at birth of the female populations collected in the Human Mortality Database. We …
Time series analysis and modeling to forecast: A survey
F Dama, C Sinoquet - arxiv preprint arxiv:2104.00164, 2021 - arxiv.org
Time series modeling for predictive purpose has been an active research area of machine
learning for many years. However, no sufficiently comprehensive and meanwhile …
learning for many years. However, no sufficiently comprehensive and meanwhile …
[HTML][HTML] Quantile cross-spectral density: A novel and effective tool for clustering multivariate time series
Á López-Oriona, JA Vilar - Expert Systems with Applications, 2021 - Elsevier
Clustering of multivariate time series is a central problem in data mining with applications in
many fields. Frequently, the clustering target is to identify groups of series generated by the …
many fields. Frequently, the clustering target is to identify groups of series generated by the …
Weighted score-driven fuzzy clustering of time series with a financial application
Time series data are commonly clustered based on their distributional characteristics. The
moments play a central role among such characteristics because of their relevant …
moments play a central role among such characteristics because of their relevant …