Time series forecasting with change point detection I have a number of daily time series to be forecasted for a horizon of one week, i.e. 7 days, in an online, automated way. A lot of times the series change due to some exogenous factors that I cannot control. What would be a good way of combining a change point detection algorithm with a forecasting technique to update my forecasts? 
The first thing that came into my mind is once the change has been detected, keep the previous forecast for some time until enough data are available and then retrain the model on the new data, discarding all the previous ones.
 A: In my opinion, changepoint detection has received it's most thorough treatment in the context of sequential models. The "bible" for sequential analysis is probably last year's (2014) book Sequential Analysis: Hypothesis Testing and Changepoint Detection by Alexander Tartakovsky. It is magisterial and seemingly exhaustive in its coverage of the topic.
http://www.amazon.com/Sequential-Analysis-Hypothesis-Changepoint-Probability-ebook/dp/B00MMOIWTS/ref=sr_1_1?ie=UTF8&qid=1445511005&sr=8-1&keywords=sequential+analysis+tartakovsky
In addition, last June Columbia sponsored The Fifth International Workshop in Sequential Methodologies which brought together some of the most prominent and prolific practitioners in the field. Tartakovsky was on the organizing committee.
https://sites.google.com/site/iwsm2015/home
See the "Detailed Program" link on the conference website for abstracts and papers. A number of automated machine learning, time series solutions are presented and discussed.  
A: You might want to read Quantifying effect of a categorical variable in time series analysis for some information about change point detection. Recent "Unusual Values" can also  be a clue to a change in error variance or a regime change in model/parameters. Depending on the identified/suggested/most likely cause for the change point different remedies might be in order including model reformation in order to create new forecasts.
You might want to peruse http://www.unc.edu/~jbhill/tsay.pdf which details how to program Pulse/Level-Step shifts and change points in error variance. I have personally extended this to Trend Change Detection ( see Auto-regression versus linear regression of x(t)-with-t for modelling time series ) and parameter change point detection ala Gregory Chow https://en.wikipedia.org/wiki/Chow_test
It is interesting to me that the history of detection is often ignored by others
. Here is some material from the Tsay reference.

