In this chapter, we explored time series data. A time series constitutes a sequence of observations on a phenomenon. In a time series, we can identify several components: trend, seasonality, cycle, and residual. We learned how to remove seasonality from a time series with a practical example.
Then the most used models to represent time series were addressed: AR, MA, ARMA, and ARIMA. For each one, the basic concepts were analyzed and then a mathematical formulation of the model was provided.
Finally, an LSTM model for time series analysis was proposed. Using a practical example, we could see how to deal with a time series regression problem with a recurrent neural network model of the LSTM type.
