Module 15: Time Series Analysis
Learn to analyze and forecast time-dependent data, from basic statistical methods to advanced deep learning approaches.
What You'll Learn
- Time Series Fundamentals
- Statistical Methods (ARIMA, SARIMA)
- Deep Learning for Time Series (LSTM, GRU, Transformers)
- Feature Engineering for Time Series
- Evaluation and Validation for Time Series
Topics Covered
1. Time Series Fundamentals
- What is Time Series Data?
- Components: Trend, Seasonality, Cyclical, Noise
- Stationarity and Differencing
- Time Series Decomposition
- Autocorrelation and Partial Autocorrelation
2. Statistical Methods
- ARIMA Models: AutoRegressive Integrated Moving Average
- SARIMA: Seasonal ARIMA
- Exponential Smoothing: Simple, Double, Triple (Holt-Winters)
- Prophet: Facebook's time series forecasting tool
3. Deep Learning for Time Series
- LSTM: Long Short-Term Memory networks
- GRU: Gated Recurrent Units
- CNN for Time Series: 1D Convolutions
- Transformers: Attention mechanisms for sequences
4. Feature Engineering
- Lag Features
- Rolling Statistics (mean, std, min, max)
- Time-based Features (hour, day, month, season)
- Fourier Features (capturing seasonality)
- External Features
5. Evaluation and Validation
- Time-based Splitting (no random split!)
- Walk-Forward Validation
- Metrics: RMSE, MAE, MAPE, SMAPE
- Cross-Validation for Time Series
Additional Resources:
- Advanced Topics →: Multivariate time series, state space models, advanced deep learning
- Project Tutorial →: Step-by-step stock price forecasting project
- Quick Reference →: Quick lookup guide for time series analysis
Learning Objectives
By the end of this module, you should be able to:
- Understand time series components and patterns
- Apply statistical methods (ARIMA, SARIMA) for forecasting
- Build deep learning models for time series
- Engineer features for time-dependent data
- Properly evaluate time series models
Prerequisites
Before starting this module, you should have completed:
- Module 01: Python for Data Science (essential path: NumPy, Pandas)
- Module 05: Model Evaluation (for proper splits), or read evaluation section first
- Module 09–10: only required for deep learning sections (LSTM/GRU)
Overlapping content: This module overlaps with intermediate project 6 and advanced project 3. Pick one path: TIME_SERIES_LEARNING_PATH.md.
Time Estimate
3-4 weeks
Projects
- Stock Price Forecasting: Predict future stock prices
- Sales Forecasting: Forecast product sales
- Energy Demand Prediction: Predict electricity consumption
- Weather Forecasting: Predict temperature/rainfall
Key Concepts
- Time Series: Data points collected over time
- Stationarity: Statistical properties don't change over time
- Autocorrelation: Correlation with lagged values
- Forecasting: Predicting future values
- Anomaly Detection: Finding unusual patterns