Improve your machine learning forecasts with accurate data splitting, time series cross-validation, feature engineering, and more!
(Yes, I tried generating time series charts with an ai tool. I'm actually surprised by the result.)
Analyzing time series data, most of the time, is not easy.
This type of data has unique quirks and challenges which are not normally found in other data sets.
For example, the temporary order of observations must be respectedand when data scientists don't take that into account, model performance is poor or, worse, completely misleading predictions.
We will address these challenges using a real data set, ensuring that the results are reproducible through the code examples provided in this article.
Without proper treatment of time series data, you run the risk of creating a model that appears to work during training, but breaks down in real-world applications.
