Time series forecasting is the task of predicting future values of a time series (as well as uncertainty bounds).
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This work proposes a novel approach for multiple time series forecasting.
Recurrent and convolutional neural networks are the most common architectures used for time series forecasting in deep learning literature.
We focus on solving the univariate times series point forecasting problem using deep learning.
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.
Multivariate time series forecasting is an important yet challenging problem in machine learning.
We introduce a differentiable loss function suitable for training deep neural nets, and provide a custom back-prop implementation for speeding up optimization.
Forecasting high-dimensional time series plays a crucial role in many applications such as demand forecasting and financial predictions.
Although many researchers have developed hybrid models or variant models based on deep learning techniques, there is no comprehensive and sound comparison among the deep learning models in this field currently.