Time Series Analysis

Learning Outcomes

After completing this course, the students will be able to: Select forecasting model; Apply the model of time series analysis; Describe the patterns of time series; Predict time series data.

Topics

1. Time series and forecasting models
2. Exploring data patterns and choosing forecasting techniques
3. Moving average and smoothing methods : Naire models, forecasting methods based on averaging, Experimental smoothing methods
4. Time series and their components, Decomposition trend, Seasonally, Adjusted data, Forecasting a seasonal time series, The sensus II, Decomposition methods
5. Regression with time series data
6. Autocorrelation, Durbin-Watson test for serial autocorrelation, Heterocedasticity
7. The Box-Jenkins (ARIMA) methodology