Anofox Forecast — Data Preparation Cheat Sheet

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

Prep raw time series so downstream forecasting / CV has clean input: no gaps, no unwanted NULLs, no degenerate series, correct hierarchy.

Critical gotcha — materialise between _by steps

_by table functions do not chain in CTEs — under parallel execution they silently return 0 rows. Always CREATE TABLE between pipeline steps.

-- BROKEN (silent 0 rows under parallelism):
WITH step1 AS (SELECT * FROM ts_fill_gaps_by('raw', id, ds, y, '1d', MAP{}))
SELECT * FROM ts_fill_nulls_const_by('step1', id, ds, y, 0.0);

-- CORRECT:
CREATE TABLE step1 AS SELECT * FROM ts_fill_gaps_by('raw', id, ds, y, '1d', MAP{});
CREATE TABLE step2 AS SELECT * FROM ts_fill_nulls_const_by('step1', id, ds, y, 0.0);

Gap filling

ts_fill_gaps_by

Insert missing date rows (NULL value) so every series has a complete regular grid.

ts_fill_gaps_by(source VARCHAR, group_col COLUMN, date_col COLUMN, value_col COLUMN,
                frequency VARCHAR) → TABLE

No params — pure frequency-driven grid completion. Inserted rows have NULL in value_col; use ts_fill_nulls_*_by next to impute.

Frequencies: '1d', '1h', '30m', '1w', '1mo', '1q', '1y' (Polars-style) or '1 day' (DuckDB INTERVAL) or raw int (days).

CREATE TABLE gaps_filled AS
SELECT * FROM ts_fill_gaps_by('raw', product_id, ds, y, '1d');

NULL imputation

Table macros. Signatures:

Function	Signature
ts_fill_nulls_forward_by(source, group_col, date_col, value_col)	Last observation carried forward
ts_fill_nulls_backward_by(source, group_col, date_col, value_col)	Next observation carried backward
ts_fill_nulls_mean_by(source, group_col, date_col, value_col)	Per-group mean (skips NULLs)
ts_fill_nulls_const_by(source, group_col, date_col, value_col, fill_value)	Constant (e.g. 0.0 for retail counts)
ts_fill_forward_by(...)	Streaming per-group forward fill; also ts_fill_forward_operator for windowed variants
ts_mark_unknown_by	Marks values matching a sentinel as unknown
ts_fill_unknown_by	Fills previously-marked unknowns

-- Retail: zero-fill absent-day rows produced by ts_fill_gaps_by
CREATE TABLE nulls_filled AS
SELECT * FROM ts_fill_nulls_const_by('gaps_filled', product_id, ds, y, 0.0);

Series filtering — drop degenerate cases

Function	Drops when
ts_drop_constant_by	All values equal (no signal to forecast)
ts_drop_short_by(source, group_col, min_length)	Series length < min
ts_drop_gappy_by(source, group_col, value_col, max_gap_ratio)	Gap ratio exceeds threshold (0.1 = 10 %)
ts_drop_zeros_by	All zero values
ts_drop_leading_zeros_by	Trim leading zeros (product not yet launched)
ts_drop_trailing_zeros_by	Trim trailing zeros (product discontinued)
ts_drop_edge_zeros_by	Trim both leading and trailing zeros

CREATE TABLE clean AS
SELECT * FROM ts_drop_short_by('nulls_filled', product_id, 24);   -- ≥ 2 years monthly

Transforms

• ts_diff_by(source, group_col, date_col, value_col, diff_order INTEGER) — first / second differencing.
• ts_detrend_by(source, group_col, date_col, value_col, method) — remove linear or polynomial trend.
• ts_validate_timestamps_by — check for out-of-order / duplicate timestamps.

CREATE TABLE stationary AS
SELECT * FROM ts_diff_by('clean', product_id, ds, y, 1);   -- first-difference

Hierarchical key operations

Retail and IoT panels usually have composite keys (region × store × item). These macros manipulate them:

Function	Purpose
ts_combine_keys	Concatenate multiple ID columns into one, sep-delimited
ts_split_keys	Reverse — split back into components
ts_aggregate_hierarchy	Sum-aggregate up a hierarchy level (region+store+item → region+store)
ts_validate_separator	Verify separator string isn't present in the source columns

-- Combine two IDs for grouping
CREATE TABLE with_combined AS
SELECT * FROM ts_combine_keys('raw', region_id, store_id, ds, y, MAP{'sep': '|'});

-- Aggregate to store level
CREATE TABLE store_level AS
SELECT * FROM ts_aggregate_hierarchy('raw', region_id, store_id, item_id, ds, y, 2);

Canonical prep pipeline

-- 1. Regularise the time grid
CREATE OR REPLACE TABLE p1 AS
SELECT * FROM ts_fill_gaps_by('raw', product_id, ds, y, '1d');

-- 2. Impute (retail → zero; continuous → forward)
CREATE OR REPLACE TABLE p2 AS
SELECT * FROM ts_fill_nulls_const_by('p1', product_id, ds, y, 0.0);

-- 3. Drop what can't be forecast
CREATE OR REPLACE TABLE p3 AS
SELECT * FROM ts_drop_short_by('p2', product_id, 30);

CREATE OR REPLACE TABLE clean AS
SELECT * FROM ts_drop_constant_by('p3', product_id, y);

STRUCT vs MAP params

Both work everywhere. STRUCT is recommended — keeps numeric params typed:

-- STRUCT (recommended)
ts_fill_gaps_by('raw', id, ds, y, '1d', {tolerance: 0.1})

-- MAP (all strings)
ts_fill_gaps_by('raw', id, ds, y, '1d', MAP{'tolerance': '0.1'})

Empty params: MAP{} or {}.

Common patterns

• Kaggle-style split: prep on train + test together, then split at the cutoff. Prevents inconsistent gap-fill / drop decisions between splits.
• CV-ready input: ts_cv_folds_by requires the input to already be gap-filled and NULL-imputed. Always run the prep pipeline first.
• JSON extension required for a subset of prep-related functions (period detection auto-loading, etc.). Enable SET autoinstall_known_extensions=1; SET autoload_known_extensions=1; in your session.

See also: anofox-forecast-eda (quality check before prep), anofox-forecast-detection (fill gaps before detecting periods), anofox-forecast-backtest (cv_folds requires clean input), anofox-forecast-models (forecast requires clean input).

Reference docs:

• docs/api/04-data-preparation.md
• docs/guides/01-getting-started.md