Building Univariate Time Series Models Using Statistical Methods
In Chapter 8, you explored essential concepts to understand the time series process, such as decomposing time series data, detecting stationarity, applying power transformations, and testing for residual autocorrelation. These techniques will be invaluable as you dive into statistical modeling in this chapter.
When working with time series data, different methods and models can be applied, 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 consider the assumptions you need to validate and test for – for example, stationarity and residual autocorrelation – it becomes apparent why time series data is deemed complex and challenging. Thus, when modeling such a complex system, your goal is to develop an approximation that effectively captures the critical factors of interest while...
