Pattern recognition with time series analysis I'm looking for some good pointers to pattern recognition with time series. Possibly something practical that can be easily understood.
As a toy example, think about collecting data from an accelerometer of a phone. Different types of motion (running, sitting, falling down, etc..) may correspond to different patterns. This is just an example, and I'm looking for some feasible solutions in the domains of time series. 
Of course, if there are methods not based on time series, I'm open also.  
 A: Pattern recognition in time series can involve a number of components. Memory i.e. auto-dependence can be characterized via an ARIMA component (stochastic/adaptive structure). Deterministic structure such as level shifts,local time trends,pulses and seasonal pulses can be found via Intervention Detection schemes. Changes in error variance and changes in parameters of the model over time can be found via residual diagnostic checking. Furthermore patterns can respond to known events , oftentimes the response is in anticipation of the event but much more frequently when the event occurs and periods following the event. If you have a pet time series please post it and I will try and develop the underlying pattern via available software.
A: correlogram (corrgram package in R), harmonic analysis, spectral analysis (eg spec.pgram in R), depending on how you want to decompose the pattern(s). auto.arima {forecast} is a useful implementation based on the suggestion above. assuming you have univariate response.
