Time Series Prediction
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Benchmarks
Most implemented
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
AA-Forecast: Anomaly-Aware Forecast for Extreme Events
GluonTS: Probabilistic Time Series Models in Python
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Papers
Nonlinear Bias-Compensated Adaptive Filter and Its Application for Time-Series Prediction
Most existing nonlinear adaptive filtering algorithms only account for output noise, neglecting the fact that input noise is also prevalent in practice. Although the recently proposed bias-compensated kernel least mean s…
Time Series PredictionReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Series
We present a diffusion based model for asynchronous time series prediction, where the goal is to predict the next inter event time and event type. To address the inherent uncertainty of future events, we introduce ReDiTT…
Time Series PredictionMissingness as Signal: Channel-Independent Spectrogram Learning for Clinical Time Series Prediction
Clinical time series prediction in intensive care units remains challenging due to heterogeneous physiological variables and informative missingness. The presence or absence of a measurement can reflect clinical decision…
Time Series PredictionReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction
Clinical time-series data are difficult to model with methods designed for regular sequences because they exhibit irregular sampling, frequent missing values, and heterogeneous observation patterns across variables. Exis…
Time Series PredictionRareCP: Regime-Aware Retrieval for Efficient Conformal Prediction
Recent advances in uncertainty quantification for time series forecasting show that conformal prediction can provide reliable prediction intervals, yet standard conformal methods are often inefficient under temporal depe…
Time Series ForecastingTime Series PredictionTailedTS: Benchmark Dataset for Heavy-Tailed Time Series Prediction and Periodicity Quantification
We present TailedTS, a large-scale benchmark dataset derived from Wikipedia hourly page view observations throughout 2024, specifically designed to test time series forecasting models under heavy-tailed, zero-inflated, a…
Time Series ForecastingTime Series Prediction