Anofox forecast detection

Anofox Forecast — Detection & Decomposition Cheat Sheet

Extension: anofox_forecast v0.15.3 | DuckDB: v1.4.5 LTS / v1.5.4+ | Dual naming: ts_* and anofox_fcst_ts_*

Detect signal structure — seasonality is not auto-detected by the forecasters; you must run detection first and pass seasonal_period explicitly to ts_forecast_by.

Requires: json extension

Detection functions use the json extension for parameter marshalling. Enable auto-load once per session:

SET autoinstall_known_extensions = 1;
SET autoload_known_extensions = 1;

Period detection — 12 methods

ts_detect_periods_by — primary entry point

ts_detect_periods_by(source VARCHAR, group_col COLUMN, date_col COLUMN, value_col COLUMN,
                     params MAP/STRUCT) → TABLE

Output columns (group column name is preserved):

Column	Type	Description
<group_col>	(input type)	Preserved group column (e.g. product_id)
periods	STRUCT(period, confidence, strength, amplitude, phase, iteration, ...)[]	Array of detected periods
n_periods	BIGINT	Count of detected periods
primary_period	DOUBLE	Top-level primary period (convenience, avoids struct indexing)
method	VARCHAR	Method that produced the result

Params:

Key	Default	Description
method	'fft'	Detection method (see table below)
max_period	series length / 2	Upper bound on detected period
min_period	2	Lower bound

-- Default (FFT) — use the top-level primary_period column
SELECT product_id, primary_period, method
FROM ts_detect_periods_by('sales', product_id, ds, y, MAP{});

-- Autoperiod (FFT + ACF validation) — access full period list via struct-array unnest
SELECT product_id, method, unnest(periods).period AS p, unnest(periods).confidence AS conf
FROM ts_detect_periods_by('sales', product_id, ds, y,
    MAP{'method': 'autoperiod'});

Methods available

Method string	Underlying algorithm	Best for
'fft'	FFT periodogram	Clean signals, fast (default)
'acf'	Autocorrelation	Noisy signals, cyclical
'autoperiod'	FFT + ACF cross-check	General purpose, robust
'aic'	AIC criterion	Model-selection style
'lomb_scargle'	Lomb-Scargle	Irregularly sampled data
'sazed'	SAZED	Ensemble of methods
'stl'	STL decomposition	Trend + seasonal separation
'ssa'	Singular Spectrum Analysis	Multi-component
'matrix_profile'	Matrix profile	Motif-based
'cfd_autoperiod'	Clipped-FD autoperiod	Robust variant
'instantaneous'	Instantaneous frequency	Time-varying periods
'auto'	Auto-select	Unknown data

Also available as scalar functions over LIST(y ORDER BY ds): ts_autoperiod, ts_cfd_autoperiod, ts_aic_period, ts_lomb_scargle, ts_ssa_period, ts_stl_period, ts_sazed_period, ts_matrix_profile_period, ts_estimate_period_fft, ts_estimate_period_acf, ts_instantaneous_period.

ts_detect_multiple_periods — multi-seasonal series

Some series have both weekly (7) and yearly (365) seasonality — use this for hourly / high-frequency data.

SELECT id, unnest(periods) AS p
FROM ts_detect_multiple_periods_by('sales', product_id, ds, y, MAP{});

Detect-then-forecast workflow (the standard pattern)

-- Step 1: Detect per-series period (use the top-level primary_period column)
CREATE OR REPLACE TABLE periods AS
SELECT product_id, primary_period AS sp
FROM ts_detect_periods_by('sales', product_id, ds, y, MAP{});

-- Step 2: Forecast with per-group detected period
--   Common: pick the mode across the panel, apply uniformly
CREATE OR REPLACE TABLE forecasts AS
SELECT * FROM ts_forecast_by('sales', product_id, ds, y,
    'AutoETS', 14, '1d',
    MAP{'seasonal_period': (SELECT mode() WITHIN GROUP (ORDER BY sp) FROM periods)::VARCHAR}
);

Changepoint detection

ts_detect_changepoints_by — Bayesian Online Changepoint Detection (BOCD)

ts_detect_changepoints_by(source, group_col, date_col, value_col, params) → TABLE

Params:

Key	Default	Description
hazard_lambda	250.0	Hazard rate. Lower → more changepoints

Returns one row per input point with is_changepoint BOOLEAN and changepoint_probability DOUBLE.

-- Detect changepoints
SELECT product_id, ds, y, is_changepoint, changepoint_probability
FROM ts_detect_changepoints_by('sales', product_id, ds, y,
    MAP{'hazard_lambda': '100'})
WHERE is_changepoint;

Also: ts_detect_changepoints (scalar) and ts_detect_changepoints_agg (aggregate).

Seasonality analysis — classify / measure strength

ts_classify_seasonality_by — timing / modulation / strength

ts_classify_seasonality_by(source, group_col, date_col, value_col, period DOUBLE) → TABLE

Output columns (group column preserved):

Column	Type	Description
<group_col>	(input)	Preserved group column
timing_classification	VARCHAR	e.g. Regular, Weekly, Irregular
modulation_type	VARCHAR	Additive / Multiplicative / None
has_stable_timing	BOOLEAN	Peak timing regularity
timing_variability	DOUBLE	Numeric variability score
seasonal_strength	DOUBLE	∈ [0, 1]
is_seasonal	BOOLEAN	Overall verdict
cycle_strengths	DOUBLE[]	Per-cycle strengths
weak_seasons	BIGINT[]	Indices of weak seasonal peaks

SELECT product_id, timing_classification, modulation_type, is_seasonal, seasonal_strength
FROM ts_classify_seasonality_by('sales', product_id, ds, y, 7.0);

Scalar variants: ts_classify_seasonality, ts_classify_seasonality_agg.

ts_seasonal_strength, ts_seasonal_strength_windowed, ts_analyze_seasonality

Numeric measures over LIST(y ORDER BY ds). windowed variant tracks strength changes across the series.

ts_detect_seasonality, ts_detect_seasonality_changes

Detect the presence and any regime shifts in the seasonal pattern.

Peak detection & timing

ts_detect_peaks_by

ts_detect_peaks_by(source, group_col, date_col, value_col, params) → TABLE

Returns detected peak indices and values per group.

ts_analyze_peak_timing_by

ts_analyze_peak_timing_by(source, group_col, date_col, value_col, period, params) → TABLE

Measures how tightly peaks cluster at a specific phase of the seasonal cycle. Useful for retail (do peaks land on the same day of the week?).

Also: ts_detect_peaks, ts_analyze_peak_timing, ts_detect_amplitude_modulation.

Decomposition

ts_mstl_decomposition_by — Multiple Seasonal-Trend decomposition (LOESS)

ts_mstl_decomposition_by(source, group_col, date_col, value_col, params) → TABLE

seasonal_periods goes INSIDE the params (JSON-string form): MAP{'seasonal_periods': '[7, 365]'}. Returns trend, seasonal_<i>, remainder per point.

SELECT product_id, ds, trend, seasonal_1, remainder
FROM ts_mstl_decomposition_by('sales', product_id, ds, y,
    MAP{'seasonal_periods': '[7]'});

ts_decompose_seasonal (scalar) — classical additive / multiplicative

ts_detrend_by

Remove linear or polynomial trend:

ts_detrend_by(source, group_col, date_col, value_col, method) → TABLE

method: 'linear' (default), 'polynomial', 'ols'.

Gotchas

• Seasonality is NOT auto-detected by forecasters. AutoETS / AutoARIMA / AutoTheta accept a seasonal_period param — if you don't set it, they select non-seasonal variants. Run detection first, then pass explicitly.
• ts_detect_periods_by returns one row per group with a periods STRUCT. Access fields with primary_period.
• Detection needs sufficient history: FFT-family methods need ≥ 2 full cycles; ACF-family needs ≥ 3. On short-history panels, ts_detect_periods may return primary_period = 1 (no seasonality) even when a period exists.
• ts_mstl_decomposition_by seasonal_periods is a JSON string ('[7, 365]'), not a native array.

Canonical detection pipeline

-- 1. Detect periods across the panel
CREATE OR REPLACE TABLE detected AS
SELECT id, primary_period AS sp, (periods).confidence AS conf
FROM ts_detect_periods_by('sales', product_id, ds, y, MAP{'method': 'autoperiod'});

-- 2. Classify seasonality mode (additive vs multiplicative) at the modal period
CREATE OR REPLACE TABLE modes AS
SELECT id, classification, confidence
FROM ts_classify_seasonality_by('sales', product_id, ds, y,
    (SELECT mode() WITHIN GROUP (ORDER BY sp) FROM detected));

-- 3. Optional: flag changepoints for post-hoc review
CREATE OR REPLACE TABLE breaks AS
SELECT product_id, ds, is_changepoint
FROM ts_detect_changepoints_by('sales', product_id, ds, y, MAP{'hazard_lambda': '250'})
WHERE is_changepoint;

See also: anofox-forecast-data-prep (fill gaps before detection — needs regular grid), anofox-forecast-eda (trend_strength / seasonality_strength gate the need for detection), anofox-forecast-models (pass detected seasonal_period to ts_forecast_by).

Reference docs:

• docs/api/05-period-detection.md
• docs/api/05a-decomposition.md
• docs/api/05b-peak-detection.md
• docs/api/06-changepoint-detection.md