Probabilistic Time Series Forecasting
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Benchmarks
Most implemented
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
AA-Forecast: Anomaly-Aware Forecast for Extreme Events
Deep and Confident Prediction for Time Series at Uber
Probabilistic Forecasting with Temporal Convolutional Neural Network
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
Papers
DecoVAE: a Lightweight Interpretable Trend-Seasonal VAE Framework for Efficient Probabilistic Time Series Forecasting
Probabilistic time series forecasting remains challenging, largely because modeling distinct trend and seasonal dynamics requires specialized approaches. Existing methods often fail to capture the unique inner properties…
Probabilistic Time Series ForecastingCLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting
Probabilistic forecasting models are widely used for time series forecasting in domains such as energy systems, finance, medicine, and transportation. In recent years, deep generative models have shown strong results on …
Probabilistic Time Series ForecastingMultivariate Time Series ForecastingProbRes: Volatility Learning for Probabilistic Time-Series Forecasting
Probabilistic time series forecasting has attracted increasing attention in financial applications due to the need to quantify risk and uncertainty in future observations. We propose ProbRes, a post-hoc probabilistic cal…
Probabilistic Time Series ForecastingParametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting
Effectively modeling non-stationary dynamics in probabilistic multivariate time series(MTS) forecasting requires balancing expressiveness with robustness. Existing parametric approaches benefit from strong inductive bias…
Probabilistic Time Series ForecastingComputational EfficiencyBeyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecasting
Real-world time series exhibit temporally structured uncertainty: volatility clusters in turbulent regimes, dissipates in stable periods, and shifts abruptly around structural breaks. Yet many probabilistic forecasting m…
Probabilistic Time Series ForecastingNoise Titration: Exact Distributional Benchmarking for Probabilistic Time Series Forecasting
Modern time series forecasting is evaluated almost entirely through passive observation of single historical trajectories, rendering claims about a model's robustness to non-stationarity fundamentally unfalsifiable. We p…
Probabilistic Time Series Forecasting