Time series forecasting is the task of predicting future values of a time series (as well as uncertainty bounds).
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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.
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.
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.
Due to their prevalence, time series forecasting is crucial in multiple domains.
We focus on solving the univariate times series point forecasting problem using deep learning.
We introduce a differentiable loss function suitable for training deep neural nets, and provide a custom back-prop implementation for speeding up optimization.
Inspired by Memory Network proposed for solving the question-answering task, we propose a deep learning based model named Memory Time-series network (MTNet) for time series forecasting.