Description
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: 25 Sub – Topics 1 Reading time series data 2 Data cleaning 3 EDA 4 Trend 5 Noise 6 Seasonality 7 Cyclicity 8 Feature Engineering 9 Stationarity Chapter 2: Statistical Univariate Modelling Chapter Goal: The fundamentals of time series forecasting with the use of statistical modelling methods like AR, MA, ARMA, ARIMA, etc. No of pages: 25 Sub – Topics 1 AR 2 MA 3 ARMA 4 ARIMA 5 SARIMA 6 AUTO ARIMA 7 FBProphet Chapter 3: Statistical Multivariate Modelling Chapter Goal: implementing multivariate modelling techniques like HoltsWinter and SARIMAX. No of pages: 25 Sub – Topics: 1 HoltsWinter 2 ARIMAX 3 SARIMAX Chapter 4: Machine Learning Regression-Based Forecasting. Chapter Goal: Building and comparing multiple classical ML Regression algorithms for timeseries forecasting. No of pages: 25 Sub – Topics: 1 Random Forest 2 Decision Tree 3 Light GBM 4 XGBoost 5 SVM Chapter 5: Forecasting Using Deep Learning. Chapter Goal: Implementing advanced concepts like deep learning for time series forecasting from scratch. No of pages: 25 Sub – Topics: 1 LSTM 2 ANN 3 MLP
