Native V8 Connector for AI agents.

It helps your agent find repeating patterns in data like sales, traffic, or weather. It uses math to prove if your data has cycles (like weekly or monthly peaks) so you can make better forecasts.

Yes. It calculates the correlation at different time intervals. If it finds a high score at a 7-day lag, it confirms a weekly cycle for your sales.

Most AIs will give you a subjective guess based on what they see. This Connector uses the Autocorrelation Function to give you exact, mathematical coefficients that prove the pattern exists.

You can use any time-series data, including daily website hits, monthly revenue, hourly temperature readings, or even periodic server error logs.

It identifies the lag, which tells you how often the pattern repeats. For example, a 7-day lag means the pattern repeats every week.

It is a key first step. By identifying the exact seasonal cycles, you can provide your agent with the hard data it needs to build a much more accurate forecast.

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).

Typically, you should check lags up to 1/3 or 1/4 of your total dataset length. For 3 years of monthly data (36 points), check up to lag 12.

ACF requires summing the products of mean-adjusted variances across shifting array indices. LLMs cannot compute this in their latent space accurately.