KMeans-RF-LSTM Multi-Model Fusion Clustering, Random Forest Regression Price Prediction and Trading…

2026-05-18 · Source: Deep Learning on Medium · Field: Finance & Economics — Capital Markets & Investment Management, FinTech & Digital Financial Services · Depth: Intermediate, extended

Summary

A multi-model fusion approach, KMeans-RF-LSTM, was developed for financial time series analysis and stock intelligent decision support, using Coca-Cola stock data from 1962 to 2025. The project involved data acquisition and preprocessing, statistical feature analysis, and multi-model modeling. Key findings include a moderately strong positive linear correlation (Pearson coefficient 0.47) between closing price and trading volume, and peak trading activity in March and September. The LSTM model demonstrated superior short-term price prediction with an RMSE of 0.0118 and R² of 0.9909, significantly outperforming traditional regression models. Feature importance analysis using Random Forest identified high and low prices as core factors, contributing over 98% to closing price prediction, while trading volume and date information had negligible impact. The system also identified diverse trading patterns through KMeans, hierarchical clustering, and Gaussian mixture models.

Key takeaway

For Data Scientists and Machine Learning Engineers building financial prediction systems, prioritize LSTM models for short-term price forecasting due to their superior performance (R²=0.9909). Focus feature engineering on high and low price data, as these are the most impactful predictors, and consider multi-clustering algorithms to identify diverse trading patterns for enhanced strategic insights. Remember to integrate external macroeconomic factors for comprehensive real-world application.

Key insights

Multi-model fusion, particularly LSTM, excels in financial time series prediction and pattern recognition.

Principles

• High/low prices are primary drivers for stock closing price prediction.
• Market activity exhibits seasonal patterns and diverse trading behaviors.

Method

The method involves KMeans clustering for pattern recognition, Random Forest for feature importance, and LSTM for short-term price prediction, with comprehensive data preprocessing and multi-model performance comparison.

In practice

• Use Dropout layers (0.2-0.3) and EarlyStopping to prevent LSTM overfitting.
• Employ `pandas.to_datetime()` for uniform date formatting in time series.
• Quantify feature importance with Random Forest to optimize model inputs.

Topics

• KMeans Clustering
• Random Forest Regression
• LSTM Model
• Financial Time Series Analysis
• Stock Price Prediction

Best for: Data Scientist, Machine Learning Engineer, Investor

Related on AIssential

• A Few-Shot LLM Framework for Extreme Day Classification in Electricity Markets — LLMs can effectively classify electricity price spikes with limited data by interpreting natural language prompts of system conditions.
• When Linear Regression Beats Random Forest: A UK Salary Prediction Case Study — Model complexity must be earned by data, as simpler models can outperform complex ones on small datasets.
• Kaggle Solution Walkthroughs: LEAP - Atmospheric Physics using AI (ClimSim) with TeamZ Lab 数据实验室 — Group-wise fine-tuning and LSTM-based architectures significantly enhance multi-label regression performance.
• Product Sales Forecasting through Time Series Analysis (EDA) — Product sales are heavily influenced by seasonality, location, store type, and promotional activities, rather than long-term trends.
• Customer Churn Prediction on Structured Data Using FT-Transformer and Stacking Ensembles — Hybrid models combining transformers and tree ensembles offer superior, calibrated churn prediction on tabular data.
• The Boosting Paradox — Machine learning struggles in finance due to market adaptivity, adversarial competition, and irreducible uncertainty, despite theoretical power.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Deep Learning on Medium.