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Walk-Forward Optimization — Cross-Validation Technique for Time-Series Data

The basic theory and implementation of walk-forward optimization as a cross-validation technique for time-series data

10 min readSep 15, 2021

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After reading this short article, you will absolutely understand the basic theory and implementation of walk-forward optimization for time-series data modelling. The common questions like why the scientist must implement the walk-forward optimization on their time-series data will be answered.

Furthermore, in the last section, we will also demonstrate the comparison between walk-forward optimization with other cross-validation techniques commonly used for cross-section data like k-fold. Can the implementation of the walk forward make a significant impact on the model performance?

Keep reading and enjoy the trips!

Walk-forward optimization

Before talking deeper about walk-forward optimization, let’s talk about the time-series data. Why does it differ from cross-section? Basically, time-series data is one of the data types that the observation is obtained from a sequence of time. For instance, the Covid-19 data comes from its beginning in January 2020…

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Data Scientist. Tech Writer. Statistics, Data Analytics, and Computer Science Enthusiast. Portfolio & social media links at http://audhiaprilliant.github.io/