TimeGPT in Snowflake Just Got a Full Upgrade: Anomaly Detection, Explainability, and a One-Command Install

Introduction

Last year, we showed you how TimeGPT could replace Snowflake's native forecasting — delivering 50x faster runtimes and better accuracy with a single SQL call.

Today, we're taking it further. The Snowflake integration is now officially part of the nixtla Python package, and it ships with five stored procedures — forecasting, anomaly detection, SHAP-based explainability, evaluation, and finetuning — all inside your Snowflake environment.

What's New

The previous integration focused on NIXTLA_FORECAST. You can now run the full TimeGPT suite directly in Snowflake:

Procedure	What it does
`NIXTLA_FORECAST`	Zero-shot time series forecasting
`NIXTLA_DETECT_ANOMALIES`	Flag anomalous observations with confidence bounds
`NIXTLA_EXPLAIN`	Return SHAP-based feature contributions
`NIXTLA_EVALUATE`	Compute MAPE, MAE, MSE across your series
`NIXTLA_FINETUNE`	Finetune the model on your historical data; returns a finetune model ID (Python stored procedure)

And installation is now a single command after pip install nixtla[snowflake]:

python -m nixtla.scripts.snowflake_install_nixtla \
    --database MY_DB \
    --schema MY_SCHEMA \
    --stage_path MY_STAGE \
    --base_url https://api.nixtla.io

The script guides you interactively through each step: packaging, uploading to your Snowflake stage, creating network rules, storing your API key in Snowflake Secrets, and registering all procedures. Set NIXTLA_API_KEY in your environment beforehand and the script will pick it up automatically. The script connects to Snowflake using a connection named "default" from ~/.snowflake/config.toml; pass --connection_name YOUR_CONNECTION if yours is named differently.

Anomaly Detection in SQL

Detecting anomalies across thousands of time series used to require exporting data, running a Python script, and reimporting results. Now it's one SQL call:

CALL MY_DB.MY_SCHEMA.NIXTLA_DETECT_ANOMALIES(
    INPUT_DATA => 'MY_DB.MY_SCHEMA.SALES_DATA',
    PARAMS => OBJECT_CONSTRUCT(
        'level', 95,
        'freq', 'D'
    )
);

Results come back as a table with unique_id, ds, y, the model's prediction (TimeGPT), an anomaly flag ("True" or "False"), and confidence bounds (TimeGPT_lo, TimeGPT_hi). No Python. No data movement.

Forecasting with Confidence Intervals

The forecast procedure supports exogenous variables and confidence levels:

CALL MY_DB.MY_SCHEMA.NIXTLA_FORECAST(
    INPUT_DATA => 'MY_DB.MY_SCHEMA.DEMAND_DATA',
    PARAMS => OBJECT_CONSTRUCT(
        'h', 14,
        'freq', 'D',
        'level', ARRAY_CONSTRUCT(80, 95)
    )
);

Confidence intervals are returned as a VARIANT column, so you can unpack whichever level your downstream dashboard needs.

Explainability with SHAP

When you pass exogenous variables (promotions, holidays, price changes), NIXTLA_EXPLAIN tells you how much each feature contributed to the forecast:

CALL MY_DB.MY_SCHEMA.NIXTLA_EXPLAIN(
    INPUT_DATA => 'MY_DB.MY_SCHEMA.SALES_WITH_FEATURES',
    PARAMS => OBJECT_CONSTRUCT(
        'h', 14,
        'futr_exog_list', ARRAY_CONSTRUCT('promotion', 'is_holiday')
    )
);

Results are in long format — one row per (unique_id, ds, feature) — with a forecast value and a contribution value. Plugs directly into any BI tool.

Evaluation

Measure forecast accuracy without leaving Snowflake:

CALL MY_DB.MY_SCHEMA.NIXTLA_EVALUATE(
    INPUT_DATA => 'MY_DB.MY_SCHEMA.HISTORICAL_DATA',
    METRICS => ARRAY_CONSTRUCT('MAPE', 'MAE', 'MSE')
);

Returns a row per (unique_id, forecaster, metric, value), making it easy to identify which series are hardest to forecast and where finetuning would help most.

Why Run This Inside Snowflake?

Data never leaves your environment. For organizations with strict data governance or compliance requirements, this is the critical difference — no data is sent to a third-party orchestration system. The Nixtla API call is made directly from within Snowflake's compute layer via a secured network rule.

Scales automatically. Behind each procedure, Snowflake distributes the work across partitions using UDTFs. Thousands of series are processed in parallel using Snowflake's own resource management — no cluster to configure.

SQL-first teams can own it. Analysts, BI engineers, and data teams can run TimeGPT without writing a line of Python.

Getting Started

Install nixtla: pip install nixtla[snowflake]
Set your API key: export NIXTLA_API_KEY=your_key_here
Run the setup script — guided prompts walk you through each component (security integration, package upload, UDTFs, stored procedures, and example datasets)
Call SQL procedures from any interface: Snowflake's worksheet, dbt, or your BI tool

Full documentation is included in the nixtla package on PyPI.

Nixtla and Snowflake Integration

Conclusion

TimeGPT's Snowflake integration has graduated from a standalone tool to a first-class part of the nixtla SDK. Whether you need fast zero-shot forecasting, anomaly detection for operational monitoring, or SHAP-based explainability for stakeholder reporting — it's now a single pip install away, running entirely inside your existing Snowflake environment.

Nixtla GenAI