series_decompose
Decompose a numeric time series into baseline, seasonal, trend, and residual components.
Returns a 4-tuple; use tuple destructuring:
(baseline, seasonal, trend, residual) = series_decompose(series, …).
Syntax
series_decompose(series, seasonality, trend, test_points, seasonality_threshold)Parameters
Prop
Type
Given a dynamic field
This parameter is string-only. In permissive mode — the default — almost every field arrives as a dynamic, so passing one works because Berserk injects asstring: extract-or-null, the value when it really is a string and null otherwise, so the call yields null (a predicate yields false). A property bag or array is therefore not matched, and tostring() is never applied implicitly — use it explicitly to match a bag's JSON text, keys included. Strict mode rejects the dynamic instead of coercing it. See String coercion and the asXXX family.
Returns: dynamic
Examples
Example 1
print (baseline, seasonal, trend, residual) = series_decompose(
dynamic([1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0]),
3
)| baseline (dynamic) | seasonal (dynamic) | trend (dynamic) | residual (dynamic) |
|---|---|---|---|
| [1.0,2.0,3.0,1.0,2.0,3.0,1.0,2.0,3.0,1.0,2.0,3.0] | [1.0,2.0,3.0,1.0,2.0,3.0,1.0,2.0,3.0,1.0,2.0,3.0] | [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0] | [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0] |