Moving Averages
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About Moving Averages
Smooth out volatile data using rolling windows.
For each row, the tool computes a statistic (mean, sum, min, max, or standard deviation) over a sliding window of N preceding rows in a time-ordered numeric column, then writes that value into a new column. A 7-row window on daily sales, for example, produces a rolling weekly average that smooths out day-to-day noise while still tracking the underlying trend.
Category: Transformation & LogicCommon Use Cases
- Computing a 7-day moving average of daily website traffic to smooth out weekday/weekend spikes
- Calculating a 30-day rolling sum of revenue for a trailing-month KPI
- Building a rolling standard deviation of stock prices to flag periods of high volatility
Key Features
- Configurable window size (number of rows)
- Multiple statistics: rolling mean, sum, min, max, standard deviation
- Preserves original data alongside the new rolling column
Run Date Decomposition first if you need to sort or bucket by weekday/month before smoothing, then feed the smoothed series into Forecast to project future values from the denoised trend.
Frequently Asked Questions
Mean, sum, min, max, or standard deviation, calculated over a sliding window of the N rows before each row.
Yes, the window slides over consecutive rows in your time-ordered numeric column, so the column should be sorted chronologically first.
Useful for analysts turning noisy daily or transactional data into a trend line worth charting.
Read the related guide →