Time series forecasting is the task of predicting future values of a time series (as well as uncertainty bounds).
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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.
IMAGE CAPTIONING IMAGE CLASSIFICATION LANGUAGE MODELLING MACHINE TRANSLATION MULTI-LABEL CLASSIFICATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION NATURAL LANGUAGE UNDERSTANDING ONE-SHOT LEARNING SENTIMENT ANALYSIS SPEAKER VERIFICATION TEXT CLASSIFICATION TIME SERIES FORECASTING VISUAL QUESTION ANSWERING
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning.
Ranked #1 on Time Series Forecasting on ETTh1 (24)
We focus on solving the univariate times series point forecasting problem using deep learning.
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
Ranked #3 on Traffic Prediction on PeMS-M
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.
In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework to model multivariate time series data.
Time series forecasting is an important problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.