I have a time series of customers. Say, every customer has 30 observations of how may items he purchased over a period of 30 months. I would like to add a feature indicating what the general trend of 12 months. For example: on Dec 2019, I will have the trend of the whole 2019 - a "running slope". It helps me to detect customer in incline or decline. The way I do it (in R) is to calculate the slope from linear regression of the last 12 months. The thing is I don't know whether I should calculate it with or without the intercept. Example for two time series:
Slope of the first one is 1 and of the second is (-0.7) From business point of view both customers are "on the rise" and I don't get what I want.
If I give up the slope, the second time series gets 2.17 which maybe better reflects the differences, yet I'm aware that it is almost never recommended.
Am I approaching it correctly? Should other calculation take place when one wants to analyze customer's "direction"?