Online Tool Store
Time Series Resampler
Resample time series data to a coarser or finer interval, choosing how values aggregate and what happens to gaps in the series.
🔒 This tool runs entirely in your browser. Your files are never uploaded to a server.
Data & CSV
Time Series Resampler
Frontend preview — no upload or external service.
Resampled series
8,760 hourly readings resampled to 365 daily means. 12 days had missing hours and are marked partial rather than averaged over fewer readings.
How the Time Series Resampler works
- Load the series with a timestamp column and a value column.
- Choose the aggregation that matches the quantity — sum for counts and totals, mean for rates and measurements.
- Decide what to do with gaps explicitly; filling them invents data that later looks like measurement.
The method
Downsampling reduces resolution by combining values, and the right combination depends on what the number represents.
totals and counts → sum; rates, temperatures, prices → mean or last
Averaging a daily sales total gives sales per day; summing an hourly temperature gives a meaningless number. Getting this backwards is the most common resampling error.
FAQ
Sum or mean?
Sum for things that accumulate — sales, visits, rainfall. Mean or last for things that are a level at a point in time — temperature, price, queue length. Mixing them produces plausible nonsense.
Should I fill gaps?
Only if you record that you did. Forward-filling a sensor outage turns missing data into a flat line that looks like a real reading, and nobody downstream can tell the difference.
What about upsampling?
Going finer than your measurements invents resolution you never had. It is sometimes needed to align two series, but the interpolated points are estimates, not data.
How we compare
| Feature | Online Tool Store | A spreadsheet | A BI platform |
|---|---|---|---|
| Aggregation chosen per series | ✓ | Formula work | Yes |
| Gap handling explicit | ✓ | Silent | ✓ |
| Marks partial periods | ✓ | ✗ | Sometimes |
| Data stays local | ✓ | ✓ | ✗ |
Time Series Resampler forces the sum-versus-mean choice up front, because that single decision determines whether the resampled series means anything.