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Chapter 1: Getting Started with Time Series.Chapter Goal: Exploring and analyzing the timeseries data, and preprocessing it, which includes feature engineering for model building.No of pages: 25Sub - Topics1Reading time series data2Data cleaning3EDA4Trend5Noise6Seasonality7Cyclicity8Feature Engineering9Stationarity
Chapter 2: Statistical Univariate ModellingChapter Goal: The fundamentals of time series forecasting with the use of statistical modelling methods like AR, MA, ARMA, ARIMA, etc. No of pages: 25Sub - Topics1AR2MA3ARMA4ARIMA5SARIMA6AUTO ARIMA7FBProphet
Chapter 3: Statistical Multivariate ModellingChapter Goal: implementing multivariate modelling techniques like HoltsWinter and SARIMAX.No of pages: 25Sub - Topics: 1HoltsWinter 2ARIMAX3SARIMAX
Chapter 4: Machine Learning Regression-Based Forecasting.Chapter Goal: Building and comparing multiple classical ML Regression algorithms for timeseries forecasting.No of pages: 25Sub - Topics: 1Random Forest2Decision Tree3Light GBM4XGBoost5SVM
Chapter 5: Forecasting Using Deep Learning.Chapter Goal: Implementing advanced concepts like deep learning for time series forecasting from scratch.No of pages: 25Sub - Topics: 1LSTM 2ANN3MLP
