Time Series Forecasting
421 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
Subtasks
Latest papers
Gegenbauer Graph Neural Networks for Time-varying Signal Reconstruction
Reconstructing time-varying graph signals (or graph time-series imputation) is a critical problem in machine learning and signal processing with broad applications, ranging from missing data imputation in sensor networks to time-series forecasting.
D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series Forecasting
xybbo5/d-pad
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A decomposition-reconstruction-decomposition (D-R-D) module is proposed to progressively extract the information of frequencies mixed in the components, corresponding to the "deep" aspect.
An End-to-End Structure with Novel Position Mechanism and Improved EMD for Stock Forecasting
As a branch of time series forecasting, stock movement forecasting is one of the challenging problems for investors and researchers.
Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt
For the state-of-the-art (SOTA) model, the MSE is reduced by $33. 3\%$.
Explaining deep learning models for ozone pollution prediction via embedded feature selection
manjimnav/TSLayer-Ozone
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Applied Soft Computing 2024
Additionally, we tackle the feature selection problem to identify the most relevant features and periods that contribute to prediction accuracy by introducing a novel method called the Time Selection Layer in Deep Learning models, which significantly improves model performance, reduces complexity, and enhances interpretability.
Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract)
pagand/model_optimze_vessel
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The maritime industry's continuous commitment to sustainability has led to a dedicated exploration of methods to reduce vessel fuel consumption.
Is Mamba Effective for Time Series Forecasting?
For TSF tasks, these characteristics enable Mamba to comprehend hidden patterns as the Transformer and reduce computational overhead compared to the Transformer.
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.
CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning
Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data.
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.
