Time Series Algorithms Recipes : Implement Machine Learning and Deep Learning Techniques with Python

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