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

Latest papers with no code

IIP-Mixer:Intra-Inter Patch Mixing Architecture for Battery Remaining Useful Life Prediction

no code yet • 27 Mar 2024

Notably, to address the varying importance of features in RUL prediction, we introduce a weighted loss function in the MLP-Mixer-based architecture, marking the first time such an approach has been employed.

D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series Forecasting

no code yet • 26 Mar 2024

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.

Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt

no code yet • 22 Mar 2024

For the state-of-the-art (SOTA) model, the MSE is reduced by $33. 3\%$.

Grey-informed neural network for time-series forecasting

no code yet • 22 Mar 2024

To tackle these challenges, this study suggests the implementation of a grey-informed neural network (GINN).

DiffSTOCK: Probabilistic relational Stock Market Predictions using Diffusion Models

no code yet • 21 Mar 2024

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

no code yet • 21 Mar 2024

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

no code yet • 21 Mar 2024

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

no code yet • 17 Mar 2024

Time series forecasting plays a crucial role in decision-making across various domains, but it presents significant challenges.

Is Mamba Effective for Time Series Forecasting?

no code yet • 17 Mar 2024

However, due to the inefficiencies of the Transformer model and questions surrounding its ability to capture dependencies, ongoing efforts to refine the Transformer architecture persist.

Chain-structured neural architecture search for financial time series forecasting

no code yet • 15 Mar 2024

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.