An introduction to time series analysis and forecasting

Description:

Time series data is everywhere, from crime statistics to web traffic, and knowing how to work with them means being able to spot trends, understand seasonality, and forecast what happens next. Time series analysis and forecasting are among the most widely used quantitative techniques in both business and research, and these skills are increasingly in demand as more of our data arrives stamped with a date and time.

Format:

This free online workshop will run in two linked parts with a break in between. Part 1 is a concepts talk and Part 2 is a dedicated hands-on code demo. Attendees are welcome to join either or both.

The session will include:

• An introduction to time series data, and how it differs from cross-sectional and pooled data.
• The core components of time series analysis: trend, seasonality, cyclic patterns and noise.
• Decomposition and how to check a series for stationarity.
• An introduction to ARIMA/SARIMA forecasting models.
• A live coding demonstration in R, using publicly available, police-recorded crime data.
• A Q&A session.

Participants will have access to all workshop materials, including slide decks and code, via a GitHub repository, circulated ahead of the session alongside a short glossary of key terms so you can arrive already comfortable with the vocabulary.

Prerequisites: No formal prerequisites are required to attend. However, those who wish to actively participate in the coding demonstration should have access to R (and RStudio) and a basic working knowledge of R, e.g. setting a working directory, reading data in, saving manipulated data under new names, and running basic descriptive statistics.

Level: Beginner to Intermediate

Target Audience: Researchers, analysts, and anyone interested in learning how to forecast time series data

Cost:

Free

Website and registration:

Register for this course

Region:

International

Keywords:

Quantitative Data Handling and Data Analysis

Related publications and presentations from our eprints archive:

Quantitative Data Handling and Data Analysis

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