Research and Implementation of Intelligent Sales Forecast Question Answering Platform

DOI

10.2991/978-94-6463-996-4_2How to use a DOI?

Keywords

sales forecast; LangChain4j; multimodal interaction; intelligent question answering

Abstract

The accuracy and real-time nature of sales forecasts have a decisive impact on corporate supply chain management and strategic deployment. In response to the limitations of traditional methods in integrating heterogeneous data and deep semantic parsing of natural language, this study develops an intelligent forecasting QaaS platform based on the LangChain4j framework. It integrates time series pattern mining with business knowledge graph technology to build a multi-dimensional decision support system. The technical solution comprises three innovative modules: 1) Establishing a hybrid data hub, achieving unified interaction between structured and textual data through the LangChain4j semantic processing pipeline; 2) Designing a dual-engine architecture for prediction and interpretation, converting the outputs of LSTM neural networks and XGBoost algorithms into traceable decision factors, supporting causal inference chain visualization; 3) Building a multimodal interaction system, integrating dynamic knowledge graphs. Cross-industry tests show that the platform achieves an F1-score of 93.5% for composite prediction problems, a 31.7 percentage point improvement over the traditional ARIMA baseline system (61.8%), with an average response latency of ≤1.8 seconds. This solution breaks through the static analysis limitations of traditional tools, combining real-time data adaptability with interpretable decision logic, providing an end-to-end solution for corporate digital operations.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Tianyu Bai
PY  - 2026
DA  - 2026/02/15
TI  - Research and Implementation of Intelligent Sales Forecast Question Answering Platform
BT  - Proceedings of the 2025 7th Management Science Informatization and Economic Innovation Development Conference (MSIEID 2025)
PB  - Atlantis Press
SP  - 4
EP  - 16
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-996-4_2
DO  - 10.2991/978-94-6463-996-4_2
ID  - Bai2026
ER  -