Time Series Analysis and Forecasting Using IBM SPSS
The course will enable candidates to analyse time series data and forecast performances using statistical models. It will also provide them with sufficient information about the distribution of data while keeping in mind trends and seasonality. Features like standard errors, confidence intervals, and residuals will also be taught in order to use the best statistical models that predict a particular variable. This course will emphasise the graphical display of results so that candidates can visualise their forecasting models.
Benefits of the course:
Forecasting will enable candidates to have better control of their inventory. They will be able to examine trends in order to determine their peak selling and slow selling periods, which will allow them to accurately estimate how much inventory they need to keep on hand during the year. This will help prevent sales loss due to out-of-stock situations, as well as the cost associated with carrying too much inventory. The course will benefit candidates in understanding customer behaviour and demands as a function of time. It will also enhance their ability to study the performance of small and large businesses in relation to banks and finance, i.e. predicting their financial future while controlling external factors.
Duration: 3 days
Who is it for?
This course is suitable for professionals in the sectors of business, finance, accounting, health, research and science.
Pre-requisites:
- Experience with SPSS (This can be tailored in the course in case there is no experience)
- Basic understanding of regression analysis
- No previous forecasting experience required
Trainer: Dr. Aiman AL-Asem
- Lecturer and senior researcher at Kingston University- London.
- Former researcher in Manchester Metropolitan University, Bolton University.
- Research project manager with Qatar University for studying students behaviour and reaction toward school bullying
- Area of experience: Social Sciences, Psychology, Research Methods, Statistics and Business data analysis.
Course outline:
- The Basics of Forecasting
- Smoothing Time Series Data
- Outliers and Error in Time Series Data
- Automatic Forecasting with the Expert Modeller
- Assessing Model Performance
- Fitting Curves to Time Series Data
- Regression with Time Series Data
- Exponential Smoothing Models
- ARIMA Models
- Applying a Model to New Data
- Seasonal Decomposition
- Modelling Seasonality
- Intervention Analysis
- Transfer Functions in ARIMA
- Automatic Forecasting of Several Time Series