1. Data Preprocessing: Missing Value Imputation, Normalization, Outlier detection and treatment, Trend, Seasonality and Cyclic component treatment.
2. Time Series Visualization
3. Time Series Forecasting using Statistical Models
4. Time Series Forecasting using Machine Learning Models
5. Time Series Forecasting using additive and multiplicative hybrid models.
6. Time Series Forecasting using STL decomposition model.
7. Time Series Forecasting using signal processing decomposition techniques.
8. Fuzzy time series forecasting ignoring membership values.
9. Fuzzy Time Series Forecasting considering membership values.
10. Fuzzy time series forecasting employing machine learning and statistical models.

Learn techniques for handling missing values, normalization, outlier detection, and treatment in time series data.

Gain proficiency in visualizing time series data to identify patterns, trends, and seasonality.

Understand the principles and assumptions underlying classical statistical time series forecasting models.

Explore the application of machine learning algorithms in crisp and fuzzy time series forecasting.

1. Proficient in pre-processing time series.
2. Learn time series visualization techniques.
3. Understand the principles of classical statistical time series forecasting models and be able to apply them to real-world datasets.
4. Understand the crisp and fuzzy time series forecasting employing machine learning models and be able to apply them to real-world datasets.

Robert H. Shumway and David S. Stoffer, Time Series Analysis and Its Applications: With R Examples, Springer

Walter Enders, Applied Econometric Time Series, Wiley

Rob J Hyndman and George Athanasopoulos, Forecasting: Principles and Practice, OTexts

Douglas C. Montgomery, Cheryl L. Jennings, and Murat Kulahci, Introduction to Time Series Analysis and Forecasting, Wiley

Journal of Forecasting, Wiley

International Journal of Forecasting, Elsevier