Univariate Time Series Forecasting
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Benchmarks
Most implemented
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
AA-Forecast: Anomaly-Aware Forecast for Extreme Events
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
Temporal Pattern Attention for Multivariate Time Series Forecasting
Papers
Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection
Multivariate Time Series Anomaly Detection (MTSAD) is essential for reliability and safety in domains such as industrial process monitoring and financial risk management, yet conventional approaches rely on application-s…
Univariate Time Series ForecastingTime Series Anomaly DetectionZero-shot GeneralizationZero-shot Multivariate Time Series Forecasting Using Tabular Prior Fitted Networks
Tabular foundation models, particularly Prior-data Fitted Networks like TabPFN have emerged as the leading contender in a myriad of tasks ranging from data imputation to label prediction on the tabular data format surpas…
Multivariate Time Series ForecastingUnivariate Time Series ForecastingAutomated univariate time series forecasting with regression trees
This paper describes a methodology for automated univariate time series forecasting using regression trees and their ensembles: bagging and random forests. The key aspects that are addressed are: the use of an autoregres…
Univariate Time Series ForecastingPredicting the Future by Retrieving the Past
Deep learning models such as MLP, Transformer, and TCN have achieved remarkable success in univariate time series forecasting, typically relying on sliding window samples from historical data for training. However, while…
Univariate Time Series ForecastingUnsupervised Anomaly Prediction with N-BEATS and Graph Neural Network in Multi-variate Semiconductor Process Time Series
Semiconductor manufacturing is an extremely complex and precision-driven process, characterized by thousands of interdependent parameters collected across diverse tools and process steps. Multi-variate time-series analys…
Univariate Time Series ForecastingGraph Neural NetworkAnomaly DetectionExploring Accuracy Law for Deep Time Series Forecasters: An Empirical Study
Deep time series forecasting has emerged as a rapidly growing field in recent years. Despite the exponential growth of community interests, progress on standard benchmarks is often limited to marginal improvements. A com…
Univariate Time Series Forecasting