In this chapter, we learned about time series analysis and white noise. We were introduced to the concepts of random walk, autoregression, autocorrelation, and stationarity, which describes how to figure out whether data is stationary.
We also learned about differencing, taking the time series data and computing the differences between consecutive observations that lead to the formation of a new series. This chapter also talked about the AR model, which is a part of a stochastic process wherein the specific lagged values of yt are used as predictor variables and regressed on yt in order to estimate the values. We also learned two optimization parameters, namely the AR model and ARIMA model.
In the next chapter, we will learn about natural language processing.