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📈 Predictive Analytics in Practice: Regression, Time-Series and Tree Ensembles

Interactive 3D forecast chart where switching between regression, time-series and tree-ensemble models on the same simulated dataset compares prediction accuracy and error bands.

Machine Learning & Neural Networks3DModerate60 FPS

A 3D bar chart of a simulated monthly metric, split into a visible training range and a faint hidden future, with a live forecast tube whose color, curve shape, and error-band radius change as you switch between regression, time-series and tree-ensemble models.

🔬 What It Demonstrates

The same training bars can be fit three different ways: a straight regression line, a trend-plus-seasonal decomposition, or a bucketed tree ensemble. Their forecasts diverge most sharply in how each one extrapolates trend and seasonality beyond the training range.

🎮 How to Use

Pick a model, then adjust noise, trend strength, seasonality and forecast horizon. Watch the prediction tube and its translucent error band reshape, and compare the forecast RMSE against the hidden future bars.

💡 Did You Know?

Tree-based ensembles like random forests can't produce values outside the range seen during training — their forecasts flatten out beyond "today," which is why practitioners often pair them with a trend-aware model when the future is expected to keep climbing or falling.

⚙ Under the hood

Interactive 3D forecast chart where switching between regression, time-series and tree-ensemble models on the same simulated dataset compares prediction accuracy and error bands.

predictive-analyticsforecastingregressiontime-seriestree-ensemblesmachine-learning

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install