Forecasting time-series data has applications in many fields, including finance, health, etc. There are potential pitfalls when applying classic statistical and machine learning methods to time-series problems. This talk will give folks the basic toolbox to analyze time-series data and perform forecasting using statistical and machine learning models, as well as interpret and convey the outputs.
Folks who attend this session will gain a basic understanding of different time series modeling and forecasting methods, both statistical and machine learning based.
This tutorial will include: - Why it is important to consider time-series data differently than other data for modeling and forecasting. - How to process time-series data for modeling and forecasting purposes. - How to perform exploratory analysis and create informative statistical plots to better understand time-series data (e.g., auto-correlation and partial auto-correlation, covariance matrices). - Provide a couple of examples of statistical and machine learning models to perform forecasting tasks. - Detail the strengths and weaknesses of different modeling methods through examples. - How to evaluate, interpret, and convey the output from forecasting models.