Python & Forecasting

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Appreciate both the capabilities and the limitations of such computer-based techniques
• program according to good practice, applicable to all high-level languages.
• Formulate time series models and construct Python-based implementations
• Use Python functions built in various libraries to fit and analyse such models to data
• demonstrate knowledge and understanding of traditional computing algorithm analysis, design and implementation using Python.
• write structured computer programs, applicable using any high-level programming language but also relying on the special features of Python
• demonstrate skills in technical report writing.
• demonstrate knowledge and understanding of the fundamentals of writing structured computer programs, applicable to using any high-level programming language.
• carry out analysis, design and implementation of algorithms using Python.
• develop, maintain and access own library of algorithms for use in other modules or in project work.

No prior programming experience is required for the module. The Python part will cover the basic principles of programming in a high-level language. The main focus will, however, be in developing a working facility of Python. The module will cover a range of the most commonly used techniques and algorithms including technical calculations as well as data manipulation, graphics, file handling, and the use of package extensions. Practical exercises are used to reinforce the ideas taught in the module, which will enable the students to build their own library of algorithms for use in other modules or in project work.

In the Forecasting part, the module will cover Time Series Models, Decomposition, Analysis and Removal of Trends and Seasonality, Exponential Smoothing Methods (such as Single Exponential, Holt and Holt-Winters Methods), Simple and Multiple Regression Techniques, and Box-Jenkins Methods for ARIMA models.

Teaching and learning methods

The module will be taught using a mixture of lectures and computer workshops. The Python part will be made of 18 hours of computer workshops and 6 hours of lectures. The Forecasting part will have 8 hours of lectures and 8 hours of computer workshops.

Summative

This is how we’ll formally assess what you have learned in this module.