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Notebook example metric: root mean square of input - (trend + season + residual) over all samples and channels. This checks additive bookkeeping; it is not a forecast error or a component-recovery score. The separate teaching input and intermediate arrays for the animation are linked below.

How it works

Method maturity: stable. Implementation: native-backed.

A centered uniform moving average supplies the trend; equal-phase means of the detrended series supply one repeated seasonal template.

Deterministic synthetic teaching signal; not a benchmark.

Open GIF · Static PNG · Source arrays (NPZ) · Trace metadata

Follow the four steps

1. Slide a uniform window

Use uniform weights over an odd-length window. DeTime increments an even requested window by one. Its NumPy mode='same' convolution uses zero extension at both ends, so the divisor remains the full window length even near an edge.

2. Subtract the smooth trend

Compute detrended = observed - trend. Folding this array into cycle rows shows the values that share each seasonal phase.

3. Average seasonal phases

For each phase offset, average the detrended values separated by season_period samples. Repeat those means along the record. If season_period is omitted, the seasonal component is identically zero.

4. Center and form residual

Subtract the mean of the complete repeated seasonal array, then return residual = observed - trend - season. This guarantees an additive identity without establishing that each assigned component is physically correct.

Parameters that change the result

What this animation computes

Window values, zero extension, uniform weights, phase averages, and centering follow the Python source. Final arrays are checked against the public Python execution path.

Interpretation

• This is an in-package baseline, not a reproduction of a separately published forecasting model.
• Zero extension pulls endpoint moving averages toward zero. Inspect boundary behavior before using the resulting components.
• One repeated phase template cannot express arbitrary cycle-to-cycle shape changes.
• Centered smoothing and full-record phase averaging use future observations relative to earlier time points; causal forecasting requires an origin-specific pipeline.

Try the method

Use this configuration with DecompositionConfig and decompose. The animation generator supplies the teaching input and records intermediate arrays.

{
  "method": "MA_BASELINE",
  "backend": "python",
  "params": {
    "trend_window": 25,
    "season_period": 24
  }
}

See the configuration reference for the full interface and optional dependencies.

Sources and reproduction

• DeTime moving-average baseline implementation
• DeTime implementation: src/detime/methods/ma_baseline.py
• DeTime implementation: native/src/ma_baseline.cpp

Regenerate this animation with python scripts/generate_method_animations.py --methods MA_BASELINE. The reproduction guide explains the environment, numerical checks, coverage manifest, and how to inspect the stored arrays.

Code

case = CASES["MA_BASELINE"]
run_gallery_case("MA_BASELINE")

Output

MA_BASELINE: Simple moving-average baseline for smoke tests and lightweight workflows.
status: ran
input mode: univariate
trend shape: (96,)
backend: native
reconstruction RMSE: 0.00000000

Decomposition Plot