Deep Learning for Time Series

There is a wealth of opportunities in the field of deep learning when it comes to time series analysis.

The inherent flexibility of deep learning makes it especially suitable for this application. As a result, it is possible to accurately capture intricate and nonlinear patterns over time, removing the need to make assumptions about functional shapes. In nonstatistical forecasting methods, this breakthrough may revolutionize the way forward.

This article provides a brief overview of deep learning (more details will be discussed later on). In deep learning, an intricate arrangement of nodes and edges is constructed in order to create a “graph” connecting inputs. By adding weight to an edge, a value will be multiplied by the transition between two nodes. As well, the value typically undergoes a nonlinear activation function. As a result of its nonlinear activation function, it can fit extremely intricate and nonlinear data, which was previously difficult to achieve.

With the advancement of readily available hardware and the availability of vast amounts of data, deep learning has reached its zenith in the last decade. Having these elements integrated has enabled robust models to be developed to handle intricate tasks. Models with deep learning have a large parameter count, highlighting their complexity. It is useful to visualize various matrix multiplication and nonlinear transformation graphs to understand how they work. Using a decentralized optimization engine, the model’s weights will be slowly fine-tuned by assimilating small batches of data over time. In this iterative process, improvements are made continuously, leading to ever-increasing accuracy. The essence of deep learning can be summarized in this way.

Although deep learning has made significant advances in areas like image processing and natural language processing, it has not had a significant impact on forecasting. In addition to enhancing forecast accuracy, deep learning could also eliminate rigid and uniform assumptions and technical prerequisites typical of traditional forecasting models.

The challenges surrounding data preprocessing and model assumptions are solved by deep learning models. Their main advantage is that they are not dependent on stationary data. As a result, the statistical properties of the data-producing system can change over time, which is not always true for traditional models. A second benefit of deep learning models is that parameter selection is eliminated by removing the often complex and subjective process involved. A seasonal ARIMA model does not need to be analyzed or ordered in accordance with seasonality. Parameters specific to each model can then be selected and fine-tuned more easily. Last but not least, deep learning models eliminate the need to formulate a hypothesis regarding the underlying dynamics of a system. Hypothesizing possible dynamics or mechanisms that drive a system’s behavior is often crucial in state space modeling. A deep learning algorithm eliminates this need.