Univariate Time Series Forecasting

In this chapter, we’ll develop deep learning models to tackle univariate time series forecasting problems. We’ll touch on several aspects of time series preprocessing, such as preparing a time series for supervised learning and dealing with conditions such as trend or seasonality.

We’ll cover different types of models, including simple baselines such as the naïve or historical mean method. We’ll provide a brief background on a popular forecasting technique, autoregressive integrated moving average (ARIMA). Then, we’ll explain how to create a forecasting model using different types of deep learning methods. These include feedforward neural networks, long short-term memory (LSTM), gated recurrent units (GRU), Stacked LSTM, and convolutional neural networks (CNNs). You will also learn how to deal with common problems that arise in time series modeling; for example, how to deal with trend using first differences...