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 )

Libraries

Use these libraries to find Time Series Forecasting models and implementations

Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract)

pagand/model_optimze_vessel 20 Mar 2024

The maritime industry's continuous commitment to sustainability has led to a dedicated exploration of methods to reduce vessel fuel consumption.

4
20 Mar 2024

TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

atik-ahamed/timemachine 14 Mar 2024

Long-term time-series forecasting remains challenging due to the difficulty in capturing long-term dependencies, achieving linear scalability, and maintaining computational efficiency.

36
14 Mar 2024

Taming Pre-trained LLMs for Generalised Time Series Forecasting via Cross-modal Knowledge Distillation

hank0626/llata 12 Mar 2024

Recently, with the surge of the Large Language Models (LLMs), several works have attempted to introduce LLMs into time series forecasting.

14
12 Mar 2024

Koopman Ensembles for Probabilistic Time Series Forecasting

anthony-frion/sentinel2ts 11 Mar 2024

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.

2
11 Mar 2024

MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process

hundredl/mg-tsd 9 Mar 2024

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.

9
09 Mar 2024

Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting

18kiran12/tsbench 7 Mar 2024

Time series forecasting attempts to predict future events by analyzing past trends and patterns.

1
07 Mar 2024

Probing the Robustness of Time-series Forecasting Models with CounterfacTS

lluism/counterfacts 6 Mar 2024

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.

2
06 Mar 2024

Predicting Outcomes in Video Games with Long Short Term Memory Networks

kittimatechulajata/predicting-outcomes-in-two-player-games-with-lstm 24 Feb 2024

Forecasting winners in E-sports with real-time analytics has the potential to further engage audiences watching major tournament events.

0
24 Feb 2024

DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load

LSY-Cython/DiffPLF 21 Feb 2024

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.

4
21 Feb 2024

Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention

romilbert/samformer 15 Feb 2024

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.

36
15 Feb 2024