Time Series Forecasting
382 papers with code • 66 benchmarks • 27 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 )
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Latest papers
Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract)
The maritime industry's continuous commitment to sustainability has led to a dedicated exploration of methods to reduce vessel fuel consumption.
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
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
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.
Predicting Outcomes in Video Games with Long Short Term Memory Networks
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.