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 - Page 29
Improved Sales Forecasting using Trend and Seasonality Decomposition with LightGBM
Tong ZhouKnowledge Extraction with Interval Temporal Logic Decision Trees
Guido Sciavicco, Stan Ionel EduardEvaluating generation of chaotic time series by convolutional generative adversarial networks
Yuki Tanaka, Yutaka YamagutiClustering Method for Time-Series Images Using Quantum-Inspired Computing Technology
Tomoki Inoue, Koyo Kubota, Tsubasa Ikami, Yasuhiro Egami, Hiroki Nagai, Takahiro Kashikawa, Koichi Kimura, Yu Matsuda
U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series
Corinne Stucker, Vivien Sainte Fare Garnot, Konrad SchindlerForecasting Irregularly Sampled Time Series using Graphs
Vijaya Krishna Yalavarthi, Kiran Madhusudhanan, Randolf Sholz, Nourhan Ahmed, Johannes Burchert, Shayan Jawed, Stefan Born, Lars Schmidt-Thieme