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In Chapter 9, Exploratory Data Analysis and Diagnosis, you were introduced to several concepts to help you understand the time series process. Such recipes included Decomposing time series data, Detecting time series stationarity, Applying power transformations, and Testing for autocorrelation in time series data. These techniques will come in handy in the statistical modeling approach that will be discussed in this chapter.
When working with time series data, different methods and models can be used, depending on whether the time series you are working with is univariate or multivariate, seasonal or non-seasonal, stationary or non-stationary, and linear or nonlinear. If you list the assumptions you need to consider and examine – for example, stationarity and autocorrelation – it will become apparent why time series data is deemed to be complex and challenging. Thus, to model such a complex system, your goal...
