Leveraging Machine Learning Models for Market Trend Forecasting and Consumer Behaviour Insights
DOI:
https://doi.org/10.63503/j.ijaimd.2025.167Keywords:
Machine Learning, Consumer Clustering, Sentiment Analysis, Forecasting, Predictive Error, LSTM, ARIMAAbstract
Market trends and consumer behaviour are key aspects of data-driven industries that require an accurate forecast. This paper describes a hybrid machine learning system combining ensemble forecasting with consumer clustering and sentiment analysis to increase predictive power. With 95% accuracy, the model outperformed ARIMA (82%), LSTM (88%), and XGBoost (91%), and reduced the average predictive error to 6.5 units, vs. 12.5 used by ARIMA. The short-term processes were used to follow the demand between 120-185 and the long-term estimates provided a consistent 118-180. The use of consumer segmentation resulted in three groups (budget (2535 spending score), balanced (4055), and premium (7088)) facilitating focused strategies. On the more positive weeks, sentiment changes added 3-5% to overall forecasts representing psychological demand. The Hybrid model took 2.8 seconds per cycle compared to IQMA taking 1.2; however, its accuracy gains of between 13 and 18% prove its worth as a robust and overall system of business intelligence.
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