Time Series Analysis and Forecasting PDF

Module 5: Time Series Analysis and Forecasting 1 Confidential Introduction 01 Applications and benefits of time series forecasting 02 Definition and importance of time series analysis  Components of Time Series Trend...

Module 5: Time Series Analysis and Forecasting 1 Confidential Introduction 01 Applications and benefits of time series forecasting 02 Definition and importance of time series analysis  Components of Time Series Trend Cyclic patterns 02 01 03 Seasonality Irregular or random fluctuations 04 Methods for Time Series Analysis Moving averages Exponential smoothing ARIMA models  Time Series Forecasting Techniques Naive method 01 02 Simple exponential smoothing Holt-Winters method 03  Evaluation of Forecasting Models Root mean squared error Mean absolute error (MAE) (RMSE) 0 1 04 02 Mean absolute percentage Mean squared error (MSE) error (MAPE) 03 Time Series Decomposition 01 Residual analysis 02 Trend estimation 03 Seasonal adjustment Advanced Time Series Techniques ARIMAGARCH models Vector autoregression (VAR) State space models  Forecasting Accuracy Accuracy vs. precision 01 02 Challenges and limitations of forecasting Improving forecasting 03 accuracy  Real-world Applications 01 Sales forecasting 02 Finance and stock market forecasting 03 Demand forecasting 04 Energy demand forecasting Conclusion 01 Future trends in time series analysis and forecasting 02 Summary of key points Introduction Introduction to different types of time series 01 models Overview of time series analysis 02 03 Importance of forecasting ARIMA Model Definition of ARIMA model ARIMA model components: AutoRegressive (AR), Integrated (I), Moving Average (MA) Steps to fit and forecast using ARIMA model Seasonal ARIMA (SARIMA) Model Steps to fit and forecast using SARIMA model 01 Definition of SARIMA model 02 03 Seasonal components in time series Exponential Smoothing Models Application of exponential smoothing in 01 forecasting Definition of exponential smoothing 02 03 Different types of exponential smoothing models: Simple, Holt's Linear, Holt-Winters Vector Autoregression (VAR) Model Steps to fit and forecast using VAR model 01 Introduction to VAR model 02 03 Application of VAR model in time series analysis GARCH Model Definition of GARCH model Volatility clustering in financial time series Steps to fit and forecast using GARCH model Machine Learning Models for Time Series Comparison of traditional methods and machine 01 learning methods Overview of machine learning models for time 02 series 03 Applications of machine learning in time series forecasting Ensemble Forecasting Advantages and challenges of ensemble 01 forecasting Combining different time series models for 02 improved forecasting 03 Introduction to ensemble forecasting Case Studies Lessons learned from implementing advanced 01 time series models Real-world examples of advanced time series 02 models 03 Success stories and challenges Conclusion Summary of advanced time series models Future directions in time series analysis Closing remarks Thank You Confidential