Description
Book Synopsis
Chapter 1: Getting Started with Time Series.- Chapter 2: Statistical Univariate Modelling.-
Chapter 3: Statistical Multivariate Modelling.- Chapter 4: Machine Learning Regression-Based Forecasting.- Chapter 5: Forecasting Using Deep Learning.
Table of ContentsChapter 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 - Topics 1 Reading time series data2 Data cleaning3 EDA4 Trend5 Noise6 Seasonality7 Cyclicity8 Feature Engineering9 Stationarity
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 - Topics 1 AR2 MA3 ARMA4 ARIMA5 SARIMA6 AUTO ARIMA7 FBProphet
Chapter 3: Statistical Multivariate ModellingChapter Goal: implementing multivariate modelling techniques like HoltsWinter and SARIMAX.No of pages: 25Sub - Topics: 1 HoltsWinter 2 ARIMAX3 SARIMAX
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: 1 Random Forest2 Decision Tree3 Light GBM4 XGBoost5 SVM
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: 1 LSTM 2 ANN3 MLP
