Time Series Forecasting
408 papers with code • 66 benchmarks • 28 datasets
Time Series Forecasting is the task of fitting a model to historical, time-stamped data in order to predict future values. Traditional approaches include moving average, exponential smoothing, and ARIMA, though models as various as RNNs, Transformers, or XGBoost can also be applied. The most popular benchmark is the ETTh1 dataset. Models are typically evaluated using the Mean Square Error (MSE) or Root Mean Square Error (RMSE).
( Image credit: ThaiBinh Nguyen )
Benchmarks
These leaderboards are used to track progress in Time Series Forecasting
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Libraries
Use these libraries to find Time Series Forecasting models and implementationsDatasets
ETT
M4
Appliances Energy
PeMSD7
EarthNet2021
ExtMarker
Subtasks
Latest papers
Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting
Time series forecasting attempts to predict future events by analyzing past trends and patterns.
Probing the Robustness of Time-series Forecasting Models with CounterfacTS
Because most of the training data does not reflect such changes, the models present poor performance on the new out-of-distribution scenarios and, therefore, the impact of such events cannot be reliably anticipated ahead of time.
Hybridizing Traditional and Next-Generation Reservoir Computing to Accurately and Efficiently Forecast Dynamical Systems
Under these conditions, we show for several chaotic systems that the hybrid RC-NGRC method with a small reservoir ($N \approx 100$) can achieve prediction performance rivaling that of a pure RC with a much larger reservoir ($N \approx 1000$), illustrating that the hybrid approach offers significant gains in computational efficiency over traditional RCs while simultaneously addressing some of the limitations of NGRCs.
Predicting Outcomes in Video Games with Long Short Term Memory Networks
kittimatechulajata/predicting-outcomes-in-two-player-games-with-lstm
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Forecasting winners in E-sports with real-time analytics has the potential to further engage audiences watching major tournament events.
DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load
Accordingly, we devise a novel Diffusion model termed DiffPLF for Probabilistic Load Forecasting of EV charging, which can explicitly approximate the predictive load distribution conditioned on historical data and related covariates.
Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multivariate long-term forecasting.
Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction
cfeng783/gtt
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We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting.
Bellman Conformal Inference: Calibrating Prediction Intervals For Time Series
We introduce Bellman Conformal Inference (BCI), a framework that wraps around any time series forecasting models and provides approximately calibrated prediction intervals.
Unified Training of Universal Time Series Forecasting Transformers
SalesforceAIResearch/uni2ts
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Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models.
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
decisionintelligence/pathformer
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Multi-scale division divides the time series into different temporal resolutions using patches of various sizes.
