Time Series Analysis: ARIMA, Prophet, ML Forecasting

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:

1. Trend: Long-term increase or decrease
2. Seasonality: Regular patterns (daily, weekly, yearly)
3. Residual: Random noise

2. Simple Forecasting Methods

Moving Average

Exponential Smoothing

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)

4. Temporal Cross-Validation

Critical: Never shuffle time series data! Use forward chaining (walk-forward validation).

5. Forecast Evaluation Metrics

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!