BACK2SERIES: Designing a Backtesting Framework for Time Series Forecasting into a RESTful API - Olinto, Eduardo; Baffa, Augusto; Pereira, Juliana Alves (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI); 2026: Anais do XXII Simpósio Brasileiro de Sistemas de InformaçãoDO - 10.5753/sbsi.2026.248292, 2026)
Research Context: Time series forecasting plays a crucial role in economics, finance, and other domains where accurately anticipating future outcomes is essential for informed decision-making. However, evaluating time series regression models requires specific methods to preserve temporal dependencies and ensure realistic assessments. Scientific and/or Practical Problem: Common validation approaches, such as random cross-validation, ignore time order, generating look-ahead bias and unreliable results. Existing libraries like skforecast and sktime provide partial solutions but lack comprehensive backtesting features, standardized procedures, and user-friendly interfaces for non-programmers. Proposed Solution and/or Analysis: This study introduces BACK2SERIES, a modular RESTful API for time series regression backtesting. It supports multiple scikit-learn estimators, expanding and rolling windows, periodic hyperparameter tuning, and proper data standardization to prevent information leakage. Its API architecture allows integration with Large Language Models (LLMs) for automated workflows. Related IS Theory: This work is grounded in Socio-technical Theory, as it integrates technical automation with human interaction, balancing system efficiency and user accessibility. It also draws on Process Virtualization Theory, enabling remote, automated, and reproducible workflows that make time series forecasting more accessible to both technical and non-technical users. Research Method: Following an exploratory case study methodology, the system was implemented in Python using FastAPI, Pandas, and scikit-learn. A case study was conducted forecasting the USD/BRL exchange rate using other emerging market currencies to validate the framework’s reproducibility and automation capabilities. Summary of Results: The API produced unbiased evaluations, automated hyperparameter tuning, and consistent model comparisons. Tests of LLM interaction confirmed that structured prompts enable complete, valid API calls. Contributions and Impact to IS Area: BACK2SERIES advances the Information Systems field by bridging rigorous time series evaluation with accessible deployment. It enables reproducible, automated, and explainable forecasting workflows, supporting both technical and non-technical users in data-driven decision-making.