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

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Unit DS05: Time Series Analysis 

QUALIFI Level 7 Diploma in Data Science

Unit code: D/618/4974 RQF
level: 7

The objective of this unit is to discuss time series forecasting methods. Learners will analyse and forecast macroeconomic variables such as GDP and inflation. Panel data regression methods will also be discussed in this unit.

Learning Outcomes and Assessment Criteria

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.

Assessment Guidance
To demonstrate all learning outcomes and assessment criteria, each unit should follow the same assessment methodology:

  • Formative: Weekly assignments focussing on knowledge and understanding of technical skills using sample data sets over a period of 2 weeks and participation in weekly live classrooms and discussion groups;
  • Summative: 1. Formal timed exam testing technical knowledge 2. Component of two individual course projects based on real word data analytics

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