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.
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
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Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
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Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.
We focus on solving the univariate times series point forecasting problem using deep learning.
To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism.
Due to their prevalence, time series forecasting is crucial in multiple domains.
This model can learn multi-range and multi-level features from time series data, and has higher predictive accuracy compared those models using fixed time intervals.