I have an awesome textbook on statistics. It covers most statistical things, but one of the things you will not find in this awesome textbook is anything on time series. Time series are different and that makes them really interesting to me. This is because the x-axis is time, with the y-axis the thing (KPI) you are measuring. Because of that, you need to use different models to predict outcomes.
In these models, the y-axis values are compared to other values on the y-axis (the lag values). For example, this month’s revenue is compared to last month’s revenue and the revenue of the month before, going all the way back to the beginning of the dataset. The chosen comparisons are based on the statistically significant lags. Last month’s revenue might be statistically significant to the current month’s revenue, but revenue two months ago may not be (but revenue from three months ago might be).
