MCP For Claude Marketplace
Time-Series Seasonality Engine

Time-Series Seasonality Engine MCP Connector for Claude

A+

Compute exact Autocorrelation (ACF) to find seasonality lags in time-series data without hallucination.

1 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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When analyzing sales data, website traffic, or temperatures, identifying the exact cyclic pattern (seasonality) is critical. Asking an LLM if data is 'seasonal' yields subjective guesses. This engine computes the Autocorrelation Function (ACF) deterministically local. By returning the exact correlation coefficients at various lags (e.g., lag 7 for weekly, lag 12 for monthly), your agent can mathematically prove the existence of cycles.

time-seriesautocorrelationseasonalitydata-scienceforecastingstatistical-analysis

1 tools expose this connector's capabilities to your AI agent.

calculate_acf_seasonality

Calculates the Autocorrelation Function (ACF) for a time-series to detect seasonality

See how to talk to your AI agent using Time-Series Seasonality Engine.

Here are daily store visitor counts for the last 60 days. Run the ACF up to lag 14 to see if there is a weekly seasonality peak at lag 7.

The computation has been executed with mathematical precision. All results are exact and ready for review.

Calculate the autocorrelation for these 48 months of revenue data. Tell me which lag has the highest correlation.

The computation has been executed with mathematical precision. All results are exact and ready for review.

Compute the ACF for these server error spikes. If all lags (1 to 10) are close to 0, confirm that the errors are completely random.

The computation has been executed with mathematical precision. All results are exact and ready for review.

Scores range from -1 to 1. A high score at Lag 7 (e.g., 0.85) means that today's value is highly correlated with the value from exactly 7 days ago (a strong weekly cycle).

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