Forecasting

Take your time series forecasting game to the next level by working through two real world scenarios in the Tidyverse!

In this post, we’ll leverage 110 years of historical data – and everything from time-series forecasting to hypothesis testing – to understand how one’s state of birth influences their name

Advanced machine learning algorithms like Artificial Neural Networks(ANNs) can’t model time-dependent data without some pre-processing. The additional processing hurdle often deters forecasters from implementing advanced methods in favor of classic (but less powerful) approaches. However, I’ve observed some notable accuracy gains applying ANNs to forecasting problems. Accordingly, this post provides a basic playbook for data cleaning, feature engineering, model selection, prediction, and risk assessment when forecasting with Neural Nets.

This post focuses on some of my favorite things – football and forecasting – and will outline how to leverage external regressors when creating forecasts. We’ll do some web scraping in R and Python to create our dataset, and then forecast how many people will visit Tom Brady’s Wikipedia page.

Sometimes a controlled experiment isn’t an option yet you want to establish causality. This post outlines a method for quantifying the effects of an intervention via counterfactual predictions.

This post covers a straightforward approach for detecting and replacing outliers in order to improve forecasting accuracy.

Early trend detection is a major area of focus in the analytics realm, because it can inform key business strategy yet it an remains extremely difficult task. This post outlines one trend-detection method in an effort to predict where a stock’s price will go in the future.