108 papers with code • 10 benchmarks • 5 datasets
Time series forecasting is the task of predicting future values of a time series (as well as uncertainty bounds).
( Image credit: DTS )
Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i. e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically -- without any prior information on how they interact with the target.
In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code.
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning.
Ranked #2 on Time Series Forecasting on ETTh2 (24)
We focus on solving the univariate times series point forecasting problem using deep learning.
We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.
Ranked #2 on Traffic Prediction on PeMS04
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.
Ranked #1 on Univariate Time Series Forecasting on Electricity
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
Ranked #3 on Traffic Prediction on PeMS-M
Time series forecasting is an important problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.
Ranked #24 on Image Generation on ImageNet 64x64
Here, we propose a general method for probabilistic time series forecasting.