[nuclearnice3]

Two thoughts.

1. There is a vast literature in "Bayesian Change Point Detection." So you might find a point where something changed and a string of negative reviews began.

https://dataorigami.net/Probabilistic-Programming-and-Bayesi...

2. Similarly, there a bajillion ways to weight recent data. One way is to increase the gain on a kalman filter. That will make recent observations more important. There are bayesian implementations of the Kalman filter.

http://stefanosnikolaidis.net/course-files/CS545/Lecture6.pd...

[rjmunro]

Is "Bayesian Change Point Detection" like https://xkcd.com/2701/?

[nuclearnice3]

Two things I like about Bayesian Change Point Detection compared to "look at the graph"

1. It can provide a probability distribution. So after running bayes, you might have multiple places where it's likely a change has taken place.

2. It generalizes to high dimensional space. It's much harder to tilt the graph in 5-d.