Time series forecasting in enterprises using an AI agent with times series MCP server

Li Chen ()
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Li Chen: Paderborn University

No 178, Working Papers CIE from Paderborn University, CIE Center for International Economics

Abstract: To address enterprise adoption challenges beyond model accuracy, including usability for non-expert users, trust and explainability, and cost efficiency, this paper proposes a hybrid architecture, in which a business AI agent acts as an orchestrator and a time series Model Context Protocol(MCP) server provides reusable forecasting capabilities along a seven-stage forecasting lifecycle. In the proposed architecture, the agent interprets the user request, reasons over the available context, invokes appropriate MCP tools, and translates structured tool outputs into business-oriented explanations. The implemented time series MCP server is evaluated on three real-world datasets under two experimental settings: a zero-shot forecasting setup, in which the agent compares the available forecasting tools, and a diagnostic-aware setup, in which the agent first analyzes data quality, seasonality, stationarity, structural breaks, and influencing factors before selecting a forecasting strategy. The results show that forecasts produced through the MCP server can reach plausible quality levels across all datasets. The diagnostic-aware workflow improved forecast accuracy and explanation quality. The experiments further show that no single model family dominates across all settings: automatic ARIMA achieved the highest aggregate accuracy score but required the highest runtime, while Chronos-2 and Toto 2.0 provided competitive accuracy-runtime trade-offs. Exponential smoothing remained a fast and interpretable baseline. The findings suggest that an MCP-based architecture can make heterogeneous forecasting methods accessible, auditable, and cost-aware for enterprise AI agents, while still requiring clear task specification, governance, and human oversight.

Keywords: time series forecasting; Model Context Protocol; AI agents; AutoML; time series foundation models; enterprise analytics; explainable forecasting; design-based research (search for similar items in EconPapers)
JEL-codes: C01 C02 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2026-06
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