Time Series
Time series analysis focuses on understanding and modeling data points collected over time, aiming to extract patterns, make predictions, and gain insights from sequential information. Current research emphasizes developing advanced model architectures, such as transformers and recurrent neural networks (RNNs/LSTMs), to handle increasingly complex, high-dimensional, and non-stationary time series data, often incorporating techniques like attention mechanisms and mixture-of-experts models for improved efficiency and accuracy. This field is crucial for numerous applications across diverse domains, including finance, healthcare, and environmental monitoring, enabling better forecasting, anomaly detection, and decision-making based on temporal data.
Papers
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree
Christina Pacher, Irene Schicker, Rosmarie deWit, Katerina Hlavackova-Schindler, Claudia Plant
Smart Journey in Istanbul: A Mobile Application in Smart Cities for Traffic Estimation by Harnessing Time Series
Senem Tanberk, Mustafa Can
Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods
Nesryne Mejri, Laura Lopez-Fuentes, Kankana Roy, Pavel Chernakov, Enjie Ghorbel, Djamila Aouada
A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection
Marc Wildi, Branka Hadji Misheva