Time Series Forecasting
397 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
Is Mamba Effective for Time Series Forecasting?
Furthermore, we conduct extensive experiments to delve deeper into the potential of Mamba compared to the Transformer in the TSF.
TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting
Long-term time-series forecasting remains challenging due to the difficulty in capturing long-term dependencies, achieving linear scalability, and maintaining computational efficiency.
Taming Pre-trained LLMs for Generalised Time Series Forecasting via Cross-modal Knowledge Distillation
hank0626/llata
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Recently, with the surge of the Large Language Models (LLMs), several works have attempted to introduce LLMs into time series forecasting.
Koopman Ensembles for Probabilistic Time Series Forecasting
In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently been proposed.
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
hundredl/mg-tsd
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However, the effective utilization of their strong modeling ability in the probabilistic time series forecasting task remains an open question, partially due to the challenge of instability arising from their stochastic nature.
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.
