Econometrics and Data Science: Apply Data Science Techniques to Model Complex Problems and Implement Solutions for Economic Problems

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2022

T. C. NokeriEconometrics and Data Sciencehttps://doi.org/10.1007/978-1-4842-7434-7_4

4. Forecasting Growth

Tshepo Chris Nokeri¹  

(1)

Pretoria, South Africa

 

Time series analysis involves examining patterns and trends in sequential data to forecast the values of a series. There is a myriad of time series models, including Autoregressive Moving Average (ARIMA) (p, d, q), which applies linear transformation between preceding and current values (autoregressive), integrative (random walk), and moving averages. One ARIMA model includes seasonality, called Seasonal ARIMA (P, D, Q) x (p, d, q). This chapter considers the additive model, which recognizes non-linearity ...