Time series data is ubiquitous: daily term structure of interest rates, tick level stock prices, daily foreign currency exchange rates, weekly initial unemployment claim, monthly company sales, daily foot traffic recorded by mobile devices, and daily number of steps taken recorded by a wearable, just to name a few.

Some of the most important and commonly used data science techniques in time series forecasting are those developed in the field of statistics and machine learning. A few basic time series statistical and machine learning modeling techniques for forecasting should be included in any data scientist’s toolkit.

This presentation discusses the application of statistical and machine learning models in real-world time series forecasting. Statistical models covered include Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, Vector Autoregressive (VAR) Model, and Regime Switching Models, and machine learning models covered include Tree-based models and Neural Network-based models. I will discuss the advantages and disadvantages when using each of these models in time series forecasting scenarios. Real-world applications, demonstrated using jupyter notebooks, are used throughout the presentation to illustrate these techniques. While not the focus in this presentation, exploratory time series data analysis will also be included in the presentation.

This presentation is suitable for data scientists who have working knowledge of the classical linear regression model and a basic understanding of univariate time series models, such as the class of Seasonal Autoregressive Integrated Moving Average Models, and machine learning techniques.