The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here
time-based moving average
Contributor II
Hi!
given a set of time-stamped data (e.g., double-precision values), what's the best way to compute time-based moving averages? For example, instead of computing the average over a window of width N, where N is the number of items in the input data array. I'd like to compute it on the items having time-stamp between times t_n and t_n+1, I suppose that filling the gaps in the data array (to get evenly-time-stamped data) would be one way of doing it: Is there any other solution that does not involve modifying the input data?
Thanks!
Alex
given a set of time-stamped data (e.g., double-precision values), what's the best way to compute time-based moving averages? For example, instead of computing the average over a window of width N, where N is the number of items in the input data array. I'd like to compute it on the items having time-stamp between times t_n and t_n+1, I suppose that filling the gaps in the data array (to get evenly-time-stamped data) would be one way of doing it: Is there any other solution that does not involve modifying the input data?
Thanks!
Alex
0
Answers
I must admit, that the ValueSeries Processing plugin still lacks this functionality. We are normally confronted with equidistant time series and I guess for learning, you will need them equidistant anyway. Filling these gaps with the moving average could be a good solution. Currently you would have to use the Fill Data Gaps operator and then build a process for filling in the missing values.
But you might post this time moving average / fill data gaps as a feature request on our bug tracker. It seems to be a good idea to me.
Greetings,
Sebastian