Rolling Standard-Deviation Plot Calculator

Analysis Results

Results update after calculation.

Not calculated

Interactive Window Inspector

Click a rolling point to inspect its calculation window.

No point selected.

Formula Used

Select a chart point for substituted values.

Automatic Interpretation

Run the calculator to generate an interpretation.

Calculated Data

Index	Timestamp or label	Series	Original value	Rolling mean	Rolling variance	Rolling deviation	Threshold status	Classification	Missing

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1. Data Input

Dataset

Paste one value column or provide label, value, and optional series columns.

Upload CSV or TXT

Sample dataset

Delimiter

Header row

Detected Columns

Timestamp or label column

Value column

Series or group column

2. Rolling Window Settings

Window type

Window size

Observations or time units.

Time unit

Window alignment

Minimum observations

Calculation step

Comparison windows

3. Deviation Method

Method

Degrees-of-freedom correction

Weight pattern

Custom weights

EWM alpha

MAD scale factor

Apply bias correction when supported

Calculate variance

4. Missing Values and Preprocessing

Missing-value handling

Required valid values (%)

Transformation

Outlier treatment

Winsorize tails (%)

Pre-smoothing window

Duplicate labels

Sort by timestamp or label

5. Volatility and Plot Controls

High-volatility threshold

Low-volatility threshold

Regime-change sensitivity

Chart height

Line width

Chart layout

Show original series

Show rolling mean

Show point markers

Shade high-volatility regions

Show range slider

Mark regime changes

6. Application Presets

Machine-learning use case

Displayed decimal places

Series selection

Import Settings

How to Use

Paste data or upload a supported file.
Select the label, value, and group columns.
Choose a rolling window and deviation method.
Configure preprocessing and volatility thresholds.
Calculate, inspect points, and export results.

Worked Example

A five-observation trailing window measures recent variability. Each point uses the current value and four earlier values. Larger deviations indicate unstable periods.

Observation	Value	Window	Rolling result
5	12	10, 11, 9, 10, 12	Calculated from five values
6	18	11, 9, 10, 12, 18	Higher variability appears

Method Notes and Limitations

Small windows react quickly but may appear noisy. Large windows produce smoother and slower changes. Choose a window matching the monitoring timescale.

Standard deviation can be distorted by extreme values. Robust MAD reduces this influence. Missing observations can also change window membership.

Thresholds are descriptive unless externally validated. They do not prove drift or model failure. Review results with domain context.

Frequently Asked Questions

What is rolling standard deviation?

It measures changing spread inside a moving data window.

Which window size should I choose?

Use a size matching the period of meaningful change.

What is a trailing window?

It uses the current observation and preceding observations.

When should I use sample deviation?

Use it when observations represent a sample from a population.

When should I use population deviation?

Use it when the window contains the complete population.

Why are early values blank?

The window may not yet contain enough valid observations.

What does MAD measure?

MAD provides a robust spread estimate using median deviations.

Can I compare multiple series?

Yes. Select a grouping column and analyze every group.

What indicates a regime change?

A large deviation shift can indicate changed variability behavior.

Can the chart prove model drift?

No. It provides evidence requiring further investigation and validation.