Taming Overfitting in Time Series Data

wikimedia.org

Table of contents

• Introduction
• Feature Selection
• Cross-Validation
• Regularization
• Conclusion

The implementation is available below:

Portfolio/time-series/prevent-overfitting.ipynb at main · smortezah/Portfolio

Introduction

Embarking on the intricate journey of time series data analysis reveals a landscape teeming with insights and complexities. Within these sequences of data lies a treasure trove of trends, fluctuations, and patterns waiting to be unveiled. However, as we delve deeper into the art of deciphering time-bound information, we encounter the hurdle of overfitting — a phenomenon where models become excessively tailored to the training data, jeopardizing their predictive power on unseen instances. In this exploration, we’ll harness Python’s capabilities to tackle overfitting head-on, delving into techniques such as feature selection, cross-validation, and regularization. By embracing tools and guidance, we’ll master the art of mitigating overfitting’s impact in time series analysis…