Dealing with correlated data
Dealing with correlated time series data in machine learning models can be challenging as traditional techniques such as random sampling can introduce biases and overlook dependencies between data points. Here are some approaches that can help:
- Time series cross-validation: Time series data is often dependent on past values and it’s important to preserve this relationship during model training and evaluation. Time series cross-validation involves splitting the data into multiple folds, with each fold consisting of a continuous block of time. This approach ensures that the model is trained on past data and evaluated on future data, which better simulates how the model will perform in real-world scenarios.
- Feature engineering: Correlated time series data can be difficult to model with traditional machine learning algorithms. Feature engineering can help transform the data into a more suitable format. Examples of feature engineering for...