Time Series Analysis and Forecasting

Learning Objective

Develop skills in applying advanced techniques for time series data analysis and forecasting.

Related Skills:

1. Preprocessing and feature engineering for time series data
2. Implementing recurrent neural networks for sequence modeling
3. Evaluating the performance of time series forecasting models

Subtopics

1. Time series data preprocessing (handling missing values, outliers, seasonality)
2. Feature engineering for time series (lags, rolling windows, external features)
3. Autoregressive and moving average models (ARIMA, SARIMA)
4. Recurrent neural networks for time series forecasting (LSTMs, GRUs)
5. Evaluating time series forecasting models (RMSE, MAPE, SMAPE)

References and Resources

- "Deep Learning for Time Series Forecasting" by Jason Brownlee
- "Time Series Analysis and Its Applications" by Robert Shumway and David Stoffer
- Coursera course "Time Series Analysis" by University of Pennsylvania