ChronoForest: An Enhanced Hybrid ARIMA–Random Forest Framework for Intelligent Sales Forecasting and Business Insight Generation in Consumer Goods

This paper presented the AI-driven intelligent sales fore-casting and insight generation system for consumer goods in regards of our proposed algorithm ChronoForest as the key contribution. The ChronoForest—a novel hybrid approach that models time series trends through ARIMA and adjusts non-linear deviations by Random Forest—is able to forecast sales with 95.8% accuracy on Superstore Sales, which is considerably higher compared to ARIMA (71.6%), Random Forest (80.7%) and ensemble baseline approach (75.9%). The effectiveness of ChronoForest is explained by its hybrid bias-variance properties and ability to learn different types of patterns including seasonal and trend as well as non-linear ones. Key contributions: introduction of ChronoForest two-stage residual correction architecture and development of pre-processing and feature engineering framework for time series prediction. Future work will focus on the inclusion of atten-tion mechanisms and transformers such as Temporal Fusion Transformer to improve dependencies modeling. The future research directions include incorporating atten-tion mechanisms with transformer architecture (for example, Temporal Fusion Transformer). Integrating auxiliary data like social media sentiments, economic factors, and weather data is another direction that would strengthen forecasting. The expansion of the framework to make it interpretable by using SHAP or LIME and providing recommendations for stock allocation, pricing, and promotions is the logical progression of this project [10].

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