Unit DS05: Time Series Analysis 

Learning Outcomes. When awarded credit for this unit, a learner will be able to:

Assessment Criteria. Assessment of this learning outcome will require a learner to demonstrate that they can:

1. Assess the concepts and uses of time series analysis and test for stationarity in time series data.

1.1 Create time series object in R and Python correctly including decomposing time series and assessing different components.
1.2 Assess whether a time series is stationary.
1.3 Transform non-stationary time series data into stationary time series data.

2. Validate ARIMA (Auto Regressive Integrated Moving Average) models and use estimation.

2.1 Identify p, d and q of ARIMA model using ACF (auto- correlation function) and a PACF (partial auto-correlation function) to describe how well values are related.
2.2 Develop ARIMA models using R and python and evaluate whether errors follow the white noise process.
2.3 Finalize the model and forecast n-period ahead to make accurate predictions.

3. Implement panel data regression methods.

3.1 Evaluate the concept of panel data regression.
3.2 Analyse the features of panel data.
3.3 Build panel data regression models in a range of contexts.
3.4 Evaluate the difference between fixed effect and random effect models.