Elevate Your Machine Learning Forecasting with Accurate Data Splitting, Time-Series Cross-Validation, Feature Engineering, and More!
(Yes, I tried to generate time-series plots with a AI tool. I’m actually surprised by the result).
Analyzing time-series data is, most of the time, not straightforward.
This kind of data has unique particularities and challenges that aren’t typically found with other datasets.
For example, the temporal order of observations must be respected, and when data scientists do not take that into account, it leads to poor model performance or, worse, entirely misleading predictions.
We will address these challenges using a real dataset, ensuring that the results are reproducible through the provided code examples in this article.
Without proper dealing with time-series data, you risk creating a model that appears to work during training but falls apart in real-world applications.