KC7015 - Time Series & Forecasting

You will learn through a series of lectures and seminars which include classroom discussions and presentations. Seminar classes will be scheduled weekly to allow exploration of the theoretical background to the techniques covered in the lectures as well as attempt the practical analysis of carefully chosen data sets. Lectures allow students to witness the development of the relevant statistical aspects behind the statistical approaches to these problems and understand how to apply the techniques and interpret the results through many examples.

Formative feedback is available weekly in the classes as you get to grip with new techniques and solve problems. In addition, we operate an open door policy where you can meet with your module tutor to seek further advice or help if required. Your ability to use the relevant theory to identify and evaluate the important factors associated with time series analysis and forecasting is assessed by a coursework and a lab-based exam at the end of the semester.

General feedback on the exam will be given in a specially-arranged feedback session in semester 2 and individual feedback will be written on scripts. An opportunity to discuss work further will be available on an individual basis when work is returned and also through the open door policy.

Direct contact with the teaching team during the lectures, seminars and practical sessions will involve participation in both general class discussions as well as one to one discussions. This gives you a chance to get immediate feedback pertinent to your particular needs in this session. Further feedback and discussion with the teaching team are also available at any time through our open door policy. In addition, all teaching materials, selected computer programmes and supplementary material (such as interesting articles or analysis tips) are available through the e-learning portal.

All modules at Northumbria include a range of reading materials that students are expected to engage with. Online reading lists (provided after enrolment) give you access to your reading material for your modules. The Library works in partnership with your module tutors to ensure you have access to the material that you need.

Knowledge & Understanding:
1. Perform appropriate tests of randomness on the data to evaluate its stationarity.
2. Evaluate the degree of trend or seasonality present in the data.

Intellectual / Professional skills & abilities:
3. Construct a suitable model after performing diagnostic tests.
4. Forecast future data values with an appropriate model and assess the level of confidence in these forecasted values.

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
5. Critically appraise the model's suitability.

SUMMATIVE



1. Coursework - 30%, 1,3
(Assignment - written report - wordcount: max 1000 words + derivations + codes + graphs and plots)

2. Examination - 70% 1,2,3,4,5
(3-hour closed-book computer- based examination with set questions and problems - wordcount: max 1000 words + derivations + codes + graphs and plots)


FORMATIVE
Formative assessment will be available on a weekly basis in the seminars through normal lecturer-student interactions and discussions around the seminar questions, allowing them to extend, consolidate and evaluate their knowledge.

Formative feedback will be provided on student work and errors in understanding will be addressed reactively using individual discussion. Solutions for seminar tasks will be provided after the students have attempted the questions, allowing students to receive feedback on the correctness of their solutions and to seek help if matters are still not clear.

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On ‘Time Series and Forecasting’, you will learn about a range of appropriate statistical techniques that are used to analyse time series data. Time series data will occur in many situations - financial, medical, business - but all these situations produce data that have common attributes. By the end of the module, you will have developed an awareness of different approaches to analysing time series data and to be able to tailor these techniques based on the initial assessment of the time series data.

You will learn through a series of lectures and seminars, which include classroom discussions and presentations, and practical computer-based sessions. Practical classes will be scheduled weekly to allow exploration of the theoretical background to the techniques covered in the lectures as well as attempt the practical analysis of carefully chosen data sets.

Direct contact with the teaching team during the lectures and seminars will involve participation in both general class discussions as well as one to one discussions during the seminars. This gives you a chance to get immediate feedback pertinent to your particular needs in this session.

You will be assessed via a coursework and lab-based examination at the end of semester.