I'm trying to understand how LINQ can be used to group data by intervals of time; and then ideally aggregate each group.
Finding numerous examples with explicit date ranges, I'm trying to group by periods such as 5-minutes, 1-hour, 1-day.
For example, I have a class that wraps a DateTime with a value:
public class Sample
{
public DateTime timestamp;
public double value;
}
These observations are contained as a series in a List collection:
List<Sample> series;
So, to group by hourly periods of time and aggregate value by average, I'm trying to do something like:
var grouped = from s in series
group s by new TimeSpan(1, 0, 0) into g
select new { timestamp = g.Key, value = g.Average(s => s.value };
This is fundamentally flawed, as it groups the TimeSpan itself. I can't understand how to use the TimeSpan (or any data type representing an interval) in the query.
You could round the time stamp to the next boundary (i.e. down to the closest 5 minute boundary in the past) and use that as your grouping:
var groups = series.GroupBy(x =>
{
var stamp = x.timestamp;
stamp = stamp.AddMinutes(-(stamp.Minute % 5));
stamp = stamp.AddMilliseconds(-stamp.Millisecond - 1000 * stamp.Second);
return stamp;
})
.Select(g => new { TimeStamp = g.Key, Value = g.Average(s => s.value) })
.ToList();
Above achieves that by using a modified time stamp in the grouping, which sets the minutes to the previous 5 minute boundary and removes the seconds and milliseconds. The same approach of course can be used for other time periods, i.e. hours and days.
Edit:
Based on this made up sample input:
var series = new List<Sample>();
series.Add(new Sample() { timestamp = DateTime.Now.AddMinutes(3) });
series.Add(new Sample() { timestamp = DateTime.Now.AddMinutes(4) });
series.Add(new Sample() { timestamp = DateTime.Now.AddMinutes(5) });
series.Add(new Sample() { timestamp = DateTime.Now.AddMinutes(6) });
series.Add(new Sample() { timestamp = DateTime.Now.AddMinutes(7) });
series.Add(new Sample() { timestamp = DateTime.Now.AddMinutes(15) });
3 groups were produced for me, one with grouping timestamp 3:05, one with 3:10 and one with 3:20 pm (your results may vary based on current time).