Time Series

This course provides an opportunity for students to learn some basic techniques in Time Series Analysis, mainly focused on examples in the world of economics and finance. This course will show how to model such data and how using those models forecasts and predictions can be made. This course will cover both traditional methods and more modern approaches to Time Series Analysis. Topics covered include, among others: Stationarity, ARIMA models, Parameter Estimation, Forecasting and Cointegration.

Accordion / FAQ component

Most modules require a 2.2 degree in a related discipline or equivalent professional experience. Should you have any queries regarding your eligibility, please contact us at info@advancecentre.ie.

N.B. Required to have completed mathematics and statistics modules that contain elements of linear algebra (specifically matrix manipulation) and calculus.

Upon completion of this module students will be able to:

  • Identify the stationarity properties of a time series,
  • Model the time series using Box-Jenkins ARIMA techniques,
  • Estimate parameters for ARIMA models using a variety of procedures,
  • Produce forecasts for a given time series,
  • Be familiar with additional topics such as cointegration, vector auto regressive models.

Learn from world renowned academic staff in Ireland’s leading, future focused and globally recognised colleges.

Gain an accredited NFQ qualification/micro credential that you may count towards a full award if you so wish in the future.

Previous modules may be used as recognition of prior learning towards Advance Centre degree programmes.

Equip yourself with the latest in demand skillset, tools, know-how and knowledge to succeed in your career.

Gain a competitive edge, influence growth and steer strategic goals in your organisation upon completion of your studies with the Advance Centre.

Yes, if you complete this module it can be credited as part of the MSc Data Analytics or Professional Diploma Data Analytics PT

Detailed Course Information

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