Hybrid Time-Series Forecasting with LangGraph, Prophet & Large Language Models (LLMs)

Author(s): Vikram Bhat

Originally published on Towards AI.

Build a Local, Explainable Pipeline with Parallel Models, Automated Evaluation (RMSE/MAE/SMAPE), and Agent Explanations.

Forecasting demand has always been a tricky mix of art and science. On one hand, classical statistical models like Prophet remain incredibly hard to beat for many real-world time series. On the other, the rise of Large Language Models (LLMs) tempts us to ask:

This article explores the construction of a fully local and explainable hybrid forecasting pipeline that integrates classical statistical models like Prophet with modern Large Language Models (LLMs). The project outlines the steps involved, including preprocessing data, running parallel forecasting branches, and evaluating their performance using RMSE, MAE, and SMAPE metrics. It emphasizes the transparency and explainability of the model’s predictions, demonstrating how each component works together to create accurate forecasts while providing clear interpretations of the results.

Read the full blog for free on Medium.

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Published via Towards AI