MCP For Claude Marketplace
Exponential Smoothing Engine

Exponential Smoothing Engine MCP Connector for Claude

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Apply Simple Exponential Smoothing (Holt-Winters family) for local deterministic time-series forecasting.

1 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

When you need to forecast the next value in a time series (like next month's sales), basic averages are too slow to react. Simple Exponential Smoothing (SES) applies an alpha factor to give recent observations exponentially more weight. This engine performs the SES recursive algorithm instantly and deterministically locally, eliminating LLM hallucination and returning a reliable mathematical T+1 forecast.

forecastingtime-seriesmathematical-modelingdeterministic-enginedata-processingstatistics

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

calculate_exponential_smoothing

Provide data array and alpha value. Applies Simple Exponential Smoothing for time-series smoothing and forecasting

See how to talk to your AI agent using Exponential Smoothing Engine.

Here are the last 12 months of MRR (revenue). Use exponential smoothing with an alpha of 0.6 to predict next month's revenue.

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

This daily active users data is very noisy. Run smoothing with a low alpha of 0.2 to establish a stable baseline.

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

Calculate the T+1 forecast twice: once with alpha 0.9 and once with alpha 0.1. Tell me how different the predictions are.

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

Alpha ranges from 0 to 1. A high alpha (e.g., 0.8) heavily weights recent data (fast reaction). A low alpha (e.g., 0.2) smooths out noise aggressively.

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