# Time-Series Seasonality Engine MCP for AI Agents AI Agent Connect > Time-Series Seasonality Engine. This Connector calculates Autocorrelation Functions (ACF) to pinpoint exact seasonal patterns in your data. Instead of guessing if your sales or traffic are cyclic, your agent uses hard math to identify specific lags, like weekly or monthly peaks, with total precision. ## Overview - **Category:** artificial-intelligence - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_SMBQyC3tcWvD9JFffA0m0Dfl8KQAaHVeN5ayz86U/ai-agent-connect - **Tags:** time-series, autocorrelation, seasonality, data-science, forecasting, statistical-analysis ## Description Identifying patterns in data like website traffic or store sales usually involves a lot of manual digging. You might see a bump every Monday, but is it actually a consistent cycle or just a fluke? If you ask an AI to guess, it will give you a subjective vibe based on the numbers it sees, which isn't reliable for serious forecasting. This Connector changes that by letting your agent run the Autocorrelation Function (ACF) directly on your datasets. It turns observations into mathematical proof. When you are looking at 12 months of revenue, the agent can tell you exactly which lag shows the strongest correlation. It takes the guesswork out of seasonality and gives you the numbers you need to build real forecasts. It is a core part of the Vinkius catalog, designed for people who need to move past maybe and into definitely. ## Tools ### calculate_acf_seasonality Calculates the Autocorrelation Function for a dataset to detect seasonality. It returns correlation coefficients at different lags so you can prove cycles. ## Prompt Examples **Prompt:** ``` Look at these daily sales for the last 90 days. Is there a weekly pattern? ``` **Response:** ``` I've analyzed the 90-day sales data using the Autocorrelation Function. Here is the breakdown of the correlation coefficients: | Lag | Correlation | | :--- | :--- | | 7 Days | **0.85** | | 14 Days | 0.82 | | 21 Days | 0.79 | The data shows a very strong weekly pattern with a peak at every 7-day interval. ``` **Prompt:** ``` Check the last 50 months of revenue. Tell me which month has the highest correlation. ``` **Response:** ``` I've run the ACF on your 50-month revenue history. The highest correlation occurs at a 12-month lag (**0.92**), which confirms a strong annual seasonality. Other notable peaks occur at 3 and 6 months, suggesting quarterly cycles as well. ``` **Prompt:** ``` Are these server errors random? Run the ACF for the last 200 pings. ``` **Response:** ``` I've checked the correlation for the error spikes. All lags from 1 to 20 are near 0.0: * Lag 1: 0.02 * Lag 5: 0.01 * Lag 10: 0.03 This confirms that the errors are completely random and not tied to any specific time cycle. ``` ## Capabilities ### Detect cyclic patterns in sales data Identify repeating trends in historical data to see if your numbers follow a regular cycle. ### Identify weekly and monthly seasonality lags Pinpoint the exact number of days or months where your data peaks most consistently. ### Calculate correlation coefficients Get the exact math score for different time intervals to prove a trend is statistically significant. ### Verify if data spikes are random Determine if a sudden jump in traffic is a one-off event or part of a recurring seasonal pattern. ### Provide mathematical proof for seasonality Move beyond visual guesses by getting hard numbers that confirm your data's seasonal behavior. ## Use Cases ### Retail Planning A manager asks the agent to find the peak shopping day for a new product launch based on 2 years of historical data. ### Web Traffic Analysis A marketing lead wants to know if a traffic spike on Sundays is a consistent weekly trend or just a one-time viral hit. ### Error Monitoring An engineer asks the agent to check if server crashes are happening on a specific schedule or if they are completely random. ### Temperature Forecasting A researcher uses the tool to find the exact monthly cycles in regional climate data for a study. ## Benefits - Stop relying on AI hallucinations for data analysis. By using calculate_acf_seasonality, your agent provides hard numbers instead of subjective guesses about your trends. - Identify exact lags quickly. You will know if a pattern repeats every 7, 14, or 30 days without manually scanning spreadsheets. - Prove seasonality to stakeholders. Use the correlation coefficients from the ACF to justify your forecasting models with actual math. - Distinguish noise from patterns. Your agent can confirm if a spike is a one-off event or a recurring seasonal trend by checking the coefficients. - Build better forecasts. Accurate seasonality detection is the foundation of any reliable time-series prediction model. ## How It Works The bottom line is your agent stops guessing and starts using math to prove your data's seasonal cycles. 1. Provide your agent with a time-series dataset like daily sales or temperature logs. 2. Ask your agent to run the ACF calculation for a specific range of lags. 3. Review the correlation coefficients to see which specific intervals show the strongest patterns. ## Frequently Asked Questions **What is the Time-Series Seasonality Engine for?** 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. **Can it find weekly patterns in my store sales?** 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. **How is this different from just asking an AI about my data?** 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. **What kind of data can I use with this?** You can use any time-series data, including daily website hits, monthly revenue, hourly temperature readings, or even periodic server error logs. **Does this tool tell me the exact date of a peak?** It identifies the lag, which tells you how often the pattern repeats. For example, a 7-day lag means the pattern repeats every week. **Can I use the Time-Series Seasonality Engine for forecasting?** 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. **What does an ACF score mean?** 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). **What is the maximum lag I should check?** 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. **Why can't Claude do this without a tool?** ACF requires summing the products of mean-adjusted variances across shifting array indices. LLMs cannot compute this in their latent space accurately.