Probabilistic Time Series Forecasting
29 papers with code • 3 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Probabilistic Time Series Forecasting
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Libraries
Use these libraries to find Probabilistic Time Series Forecasting models and implementations
Lorenz Dataset
Most implemented papers
Probabilistic Time Series Forecasting with Shape and Temporal Diversity
vincent-leguen/STRIPE
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NeurIPS 2020
We introduce the STRIPE model for representing structured diversity based on shape and time features, ensuring both probable predictions while being sharp and accurate.
Adversarial Sparse Transformer for Time Series Forecasting
hihihihiwsf/AST
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NeurIPS 2020
Specifically, AST adopts a Sparse Transformer as the generator to learn a sparse attention map for time series forecasting, and uses a discriminator to improve the prediction performance from sequence level.
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
zalandoresearch/pytorch-ts
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In this work, we propose \texttt{TimeGrad}, an autoregressive model for multivariate probabilistic time series forecasting which samples from the data distribution at each time step by estimating its gradient.
ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models
However, many existing works can not be widely used because of the constraints of functional form of generative models or the sensitivity to hyperparameters.
Probabilistic Time Series Forecasting with Implicit Quantile Networks
zalandoresearch/pytorch-ts
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Here, we propose a general method for probabilistic time series forecasting.
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
adityalab/camul
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We use CAMul for multiple domains with varied sources and modalities and show that CAMul outperforms other state-of-art probabilistic forecasting models by over 25\% in accuracy and calibration.
Temporal Convolutional Attention Neural Networks for Time Series Forecasting
flaviagiammarino/tcan-tensorflow
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International Joint Conference on Neural Networks (IJCNN) 2021
TCAN requires less number of convolutional layers than TCNN for an extended receptive field, is faster to train and is able to visualize the most important timesteps for the prediction.
Parameter Efficient Deep Probabilistic Forecasting
elephaint/pedpf
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However, these methods require a large number of parameters to be learned, which imposes high memory requirements on the computational resources for training such models.
Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting
filippomb/ensemble-conformalized-quantile-regression
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EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), which are suitable for nonstationary and heteroscedastic time series data.
Robust Probabilistic Time Series Forecasting
tetrzim/robust-probabilistic-forecasting
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Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties.
