Time Series Forecasting
396 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 with no code
DiffSTOCK: Probabilistic relational Stock Market Predictions using Diffusion Models
In this work, we propose an approach to generalize denoising diffusion probabilistic models for stock market predictions and portfolio management.
HySim: An Efficient Hybrid Similarity Measure for Patch Matching in Image Inpainting
In this sense, there is still a need for model-driven approaches in case of application constrained with data availability and quality, especially for those related for time series forecasting using image inpainting techniques.
An Analysis of Linear Time Series Forecasting Models
Despite their simplicity, linear models perform well at time series forecasting, even when pitted against deeper and more expensive models.
From Pixels to Predictions: Spectrogram and Vision Transformer for Better Time Series Forecasting
Time series forecasting plays a crucial role in decision-making across various domains, but it presents significant challenges.
Chain-structured neural architecture search for financial time series forecasting
We compare three popular neural architecture search strategies on chain-structured search spaces: Bayesian optimization, the hyperband method, and reinforcement learning in the context of financial time series forecasting.
MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer
Based on this strategy, we introduce MCformer, a multivariate time-series forecasting model with mixed channel features.
Leveraging Non-Decimated Wavelet Packet Features and Transformer Models for Time Series Forecasting
Thirdly, we evaluate the use of these wavelet features on a significantly wider set of forecasting methods than previous studies, including both temporal and non-temporal models, and both statistical and deep learning-based methods.
$\textbf{S}^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
To this end, we propose Semantic Space Informed Prompt learning with LLM ($S^2$IP-LLM) to align the pre-trained semantic space with time series embeddings space and perform time series forecasting based on learned prompts from the joint space.
RATSF: Empowering Customer Service Volume Management through Retrieval-Augmented Time-Series Forecasting
An efficient customer service management system hinges on precise forecasting of service volume.
InjectTST: A Transformer Method of Injecting Global Information into Independent Channels for Long Time Series Forecasting
A channel identifier, a global mixing module and a self-contextual attention module are devised in InjectTST.
