Duration: 2 Days
Overview: Time series data arises in applications from finance to personal activity monitoring and has unique characteristics that demand the use of specialised techniques. The course covers the fundamentals of modelling time series data and focuses on the application of the main model types used to analyse univariate time series: simple linear regression, exponential smoothing and autoregressive integrated moving average with exogenous variables (ARIMAX). This course can be delivered in R, Python, or SAS.
At Course Completion: After attending this course delegates will:
- Understand the fundamentals of time series forecasting
- Understand how simple linear regressions (SLR) can be used in a time series capacity
- Understand ARIMA models and how they can be applied
- Be able to fit a SLR, Exponential Smoothing and ARIMAX models to time series data
- Be able to accommodate trend, as well as seasonal and event-related variation, in time series models
- Be able to interpret and evaluate time series models
- Be able to identify relative strengths and weaknesses of the model types
Who Should Attend: This course is designed for people who would like to begin to work with time series data using specialist techniques, and is ideally suited to people working in data analyst, data science, business analyst, statistician, or similar roles wishing to add time series modelling skills to their repertoire.
Prerequisites: To attend this course delegates should be comfortable with basic statistical concepts, exploratory analysis techniques, and basic analytics techniques such as ANOVA and regression. In addition, delegates should be capable of using the technology through which the course is delivered (a list of specific aspects of the technology used with which delegates should be familiar is available on request).
Outline: The course will run over two days and will broadly follow the timetable shown below. The course will be delivered through presentations, real world examples, discussions, and workshops.
||Introduction to Time Series
||Simple Linear Regression for Time Series
|Exponential Smoothing Models
||ARIMA & ARIMAX Models
||Evaluating and Interpreting Time Series Models