Elevate Your Machine Learning Forecasting with Accurate Data Splitting, Time-Series Cross-Validation, Feature Engineering, and More!
(Yes, I attempted to generate time-series plots with a AI tool. I’m actually surprised by the result).
Analyzing time-series data is, more often than not, not straightforward.
This type of data has unique particularities and challenges that aren’t typically found with other datasets.
For instance, the temporal order of observations should be respected, and when data scientists don’t take that into consideration, it results in poor model performance or, worse, entirely misleading predictions.
We’ll address these challenges using an actual dataset, ensuring that the outcomes are reproducible through the provided code examples in this text.
Without proper coping with time-series data, you risk making a model that appears to work during training but falls apart in real-world applications.
