Exploratory Data Analysis

Profile your time series data before forecasting with comprehensive statistical metrics.

Function	Description
ts_stats	Compute per-series statistical metrics
ts_quality_report	Generate quality assessment from stats
ts_stats_summary	Aggregate statistics across all series

Showing 3 of 3

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anofox_fcst_ts_stats

Computes per-series statistical metrics including length, date ranges, central tendencies, dispersion, value distributions, and quality indicators for exploratory analysis and data profiling.

Parameters

Parameter	Type	Required	Description
table_name	VARCHAR	Yes	Source table
group_col	VARCHAR	Yes	Grouping column name
date_col	VARCHAR	Yes	Date/timestamp column
value_col	VARCHAR	Yes	Value column
frequency	VARCHAR/INTEGER	Yes	Time frequency ('1d', '1w', '1mo') or integer step

Output

Column	Type	Description
series_id	BIGINT	Series identifier (matches group_col)
length	BIGINT	Number of observations
start_date	DATE/TIMESTAMP	First date in series
end_date	DATE/TIMESTAMP	Last date in series
expected_length	BIGINT	Expected length based on frequency
mean	DOUBLE	Average value
std	DOUBLE	Standard deviation
min	DOUBLE	Minimum value
max	DOUBLE	Maximum value
median	DOUBLE	Median value
n_null	BIGINT	Count of NULL values
n_zeros	BIGINT	Count of zero values
n_unique_values	BIGINT	Count of distinct values
is_constant	BOOLEAN	True if series has no variation
plateau_size	BIGINT	Longest consecutive constant segment
plateau_size_non_zero	BIGINT	Longest non-zero constant segment
n_zeros_start	BIGINT	Leading zeros count
n_zeros_end	BIGINT	Trailing zeros count
n_duplicate_timestamps	HUGEINT	Duplicate date count

Example

-- Compute statistics for each product
CREATE TABLE sales_stats AS
SELECT * FROM anofox_fcst_ts_stats(
    'sales_raw',
    'product_id',
    'date',
    'amount',
    '1d'
);

-- View series with potential issues
SELECT series_id, length, n_null, is_constant
FROM sales_stats
WHERE n_null > 0 OR is_constant = true;

Use when:

• Profiling a new dataset before forecasting
• Identifying series with data quality issues
• Understanding the characteristics of your time series

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anofox_fcst_ts_quality_report

Generates quality assessment reports by evaluating series against configurable thresholds for gaps, missing values, constant series, short series, and temporal alignment.

Parameters

Parameter	Type	Required	Description
stats_table	VARCHAR	Yes	Table produced by ts_stats
min_length	INTEGER	Yes	Minimum acceptable series length

Quality Checks Performed

• Gap analysis - Missing timestamps within series
• Missing values - NULL counts
• Constant series - No variation in values
• Short series - Below minimum length threshold
• End date alignment - Series ending on different dates

Example

-- First generate statistics
CREATE TABLE stats AS
SELECT * FROM anofox_fcst_ts_stats(
    'sales',
    'product_id',
    'date',
    'amount',
    '1d'
);

-- Then generate quality report with 30-day minimum
CREATE TABLE quality AS
SELECT * FROM anofox_fcst_ts_quality_report('stats', 30);

Use when:

• Identifying series that need data preparation
• Creating data quality dashboards
• Filtering out problematic series before forecasting

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anofox_fcst_ts_stats_summary

Aggregates statistical metrics across all series from ts_stats output, computing dataset-level summaries for high-level dataset characterization.

Parameters

Parameter	Type	Required	Description
stats_table	VARCHAR	Yes	Table produced by ts_stats

Output

Column	Type	Description
total_series	INTEGER	Count of distinct series
total_observations	BIGINT	Sum of all observations
avg_series_length	DOUBLE	Mean series length
date_span	INTEGER	Number of distinct date intervals

Example

-- Generate statistics first
CREATE TABLE stats AS
SELECT * FROM anofox_fcst_ts_stats(
    'sales',
    'product_id',
    'date',
    'amount',
    '1d'
);

-- Get dataset-level summary
SELECT * FROM anofox_fcst_ts_stats_summary('stats');

-- Example output:
-- total_series | total_observations | avg_series_length | date_span
-- 150          | 45000              | 300.0             | 365

Use when:

• Getting a quick overview of your dataset
• Reporting on data characteristics
• Comparing datasets

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Complete EDA Workflow

-- 1. Compute per-series statistics
CREATE TABLE stats AS
SELECT * FROM anofox_fcst_ts_stats(
    'sales_raw',
    'product_id',
    'date',
    'amount',
    '1d'
);

-- 2. Get dataset overview
SELECT * FROM anofox_fcst_ts_stats_summary('stats');

-- 3. Generate quality report (minimum 60 days)
CREATE TABLE quality AS
SELECT * FROM anofox_fcst_ts_quality_report('stats', 60);

-- 4. Identify problematic series
SELECT series_id
FROM stats
WHERE is_constant = true
   OR length < 60
   OR n_null > length * 0.1;

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