Time series forecasting using statistical and machine learning models: When and how

Description

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 within the fields 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.

Jeffrey Yau discusses the applications of statistical time series models, such as ARIMA, VAR, and regime-switching models, and machine learning models, such as random forest and neural network-based models, to forecasting problems. You’ll learn the advantages and disadvantages when using each of these models in time series forecasting scenarios and explore real-world applications, demonstrated using Jupyter notebooks, that illustrate these techniques. And along the way, Jeffrey touches on exploratory time series data analysis.