Time Series Forecasting With Deep Learning: A Survey

While traditional methods have focused on parametric models informed by domain expertise - such as autoregressive (AR) [6], exponential smoothing [7, 8] or structural time series models [9] - modern machine learning methods provide a means to learn temporal dynamics in a purely data-driven manner [10]. With the increasing data availability and computing power in recent times, machine learning has become a vital part of the next generation of time series forecasting models. Deep learning in particular has gained popularity in recent times, inspired by notable achievements in image classification [11], natural language processing [12] and reinforcement learning [13]. By incorporating bespoke architectural assumptions - or inductive biases [14] - that reflect the nuances of underlying datasets, deep neural networks are able to learn complex data representations [15], which alleviates the need for manual feature engineering and model design. The availability of open-source backpropagation frameworks [16, 17] has also simplified the network training, allowing for the customisation for network components and loss functions.