Spotting trends in time based data I have a dataset which I need to spot trends in. The actual data refers to operation calls which take a certain amount of time to complete. My client wants to know which operation calls  are improving and which are deteriorating over a time period.
Each operation call has an entry for each day which shows the average time that call took to return. 
Date        Call    Avg duration
-----------------------------------
18/03/2013  Call 1  123ms
19/03/2013  Call 1  156ms
20/03/2013  Call 1  198ms
21/03/2013  Call 1  99ms

My current implementation uses 
(CurrentDayAvgDuration - PrevDayAvgDuration) / √(CurrentDayAvgDuration + PrevDayAvgDuration)

for each day and then takes the average of that value over all days but I'm not too sure if the value is relevant. That formula is one I found and plugged in just to try.
My question is, how can I calculate a single value per operation call which can be used in comparison with all of the other operation calls to determine which are trending upwards (deteriorating) and which are trending downwards (improving)?
 A: I'm not able to post a comment because of my weak reputation.
A simple answer would be a moving average, you simply have to replace the duration of the call by the mean duration of the last p call. You'll choose p empirically, by plotting the results, high enough to remove noise, low enough to observe something.
You should be able to observe the trend by plotting your new time serie
If you want a single value you should try: short term mean - longer term mean.
The definition of short term and long term depends on the process (noise, seasonality ... ect), the way it evolves, the number of data you have, the frequency at wich your client ask for this information.
For exemple, if your client ask for it every month you should take:
short term: 1 month
long term: 12 month
if your client ask for it every week:
short term: 1 week
long term: 1/2/3 month
So the progress will be easy to show to your client: over last month the call duration improved by xxms from last year.
A: I partially agree to Imorin's comment. But if you are interested in a trend, you must consider   rates of change over a moving window. If the Averages are successively decreasing/increasing, then it must indicate improved/deteriorated performance. 
You must also select a threshold based on historical records as to lowest/highest values of rates indicating a significant step towards improvisation or deterioration.
