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
SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion
Multivariate time series forecasting plays a crucial role in various fields such as finance, traffic management, energy, and healthcare.
Intriguing Properties of Positional Encoding in Time Series Forecasting
Motivated by these findings, we introduce two new PEs: Temporal Position Encoding (T-PE) for temporal tokens and Variable Positional Encoding (V-PE) for variable tokens.
ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting
To capitalize on both of these strengths, we propose ATFNet, an innovative framework that combines a time domain module and a frequency domain module to concurrently capture local and global dependencies in time series data.
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Next, we employ TFB to perform a thorough evaluation of 21 Univariate Time Series Forecasting (UTSF) methods on 8, 068 univariate time series and 14 Multivariate Time Series Forecasting (MTSF) methods on 25 datasets.
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
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.
