Time series forecasting is usually a complex task because the structure of already univariate data often contains many unobserved factors. Standard models such as ARIMA, or filters, e.g. Kalman Filter are complex models that often need tweaking which requires a rigorous understanding of the underlying theory.
Practitioners with good domain knowledge but little statistics know-how want to make use of machine learning and forecasting methodologies to inform their business decisions. So a number of software packages and libraries attempt to bridge this gap by offering automated solutions.
ARIMA timeseries models are often taught in econometrics courses as part of the regular business science curriculum and are thus put to use by sometimes inexperienced data scientists.
The intention of this case study is to understand the data generating process behind simple MA(1) models and illustrate weakness of the estimators at small sample sizes.
For the tested MA(1) model with coefficient beta=0.3, a time series length of at least 5000 observations is necessary to reach a narrower confidence interval.
The impact on the goodness of forecasts is evaluated and depends critically on the estimated coefficient.
Install some libraries first: