Introduction: Predicting the Future
Stock prices, weather temperatures, website traffic, energy consumption – all change over time. Time series data has a temporal dimension that traditional ML ignores!
Key Challenge: Past values influence future values. We need models that capture temporal dependencies.
Learning Objectives
- Understand time series components (trend, seasonality, noise)
- Master ARIMA models for forecasting
- Apply exponential smoothing
- Handle temporal cross-validation
- Build forecasting pipelines
- Evaluate forecast accuracy
1. Time Series Components
Decomposition
Any time series can be decomposed into:
- Trend: Long-term increase or decrease
- Seasonality: Regular patterns (daily, weekly, yearly)
- Residual: Random noise
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2. Simple Forecasting Methods
Moving Average
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Exponential Smoothing
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3. ARIMA Models
ARIMA(p, d, q): AutoRegressive Integrated Moving Average
- p: Order of autoregression (AR)
- d: Degree of differencing
- q: Order of moving average (MA)
AR (AutoRegressive)
MA (Moving Average)
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4. Temporal Cross-Validation
Critical: Never shuffle time series data! Use forward chaining (walk-forward validation).
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5. Forecast Evaluation Metrics
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Key Takeaways
✅ Time series has temporal dependencies – order matters!
✅ Components: Trend + Seasonality + Residual
✅ ARIMA models: Flexible framework for time series forecasting
✅ Temporal CV: Use forward chaining, never shuffle
✅ Metrics: MAE, RMSE, MAPE for forecast accuracy
What's Next?
Next lesson: Reinforcement Learning Introduction – agents, rewards, and learning optimal policies!