# Aggregating time series data to one variable

I have got various time series data for different observations. I want to represent one time series observation by one aggregated variable. The easiest one is to take the mean of the time series, but mean does not capture the variations in the observation over time. I thought of computing the distance (dist) between an arbitrary reference and the time series data.

I am curious to know if there is other technique that I could apply to fairly represent the time series observation by one aggregation variable? Is there any R package that I can use?

Thanks

• What you ask is very much application dependent. Can you tell us more on what you are trying to achieve? For example, if your time series represents a stock the best approach would probably be very different than if you want to characterize an EEG signal. – Leeor May 15 '14 at 12:25
• The time series are traffic data (i.e., speed, count, spacing.... of vehicles on freeways over time on each day). The goal is to summarize/aggregate each time series by one variable and then apply multi-dimensional scaling to cluster days of similar traffic characteristics. – Filly May 15 '14 at 13:48
• Well in that case why do you require only one aggregation variable? Clustering can be done a number of variables... – Leeor May 15 '14 at 13:56