Fractional Differencing for Stationarity - Quant Researcher Interview Question
Difficulty: Hard
Category: machine_learning
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Topics: time_series, stationarity, preprocessing, quantitative_finance
Problem Description
Fractional differencing is a technique used in quantitative finance to transform non-stationary time series into stationary ones while preserving memory, unlike standard integer differencing which erases long-term dependencies. By applying a real-valued differencing order $d$, this method balances the trade-off between stationarity and information retention, making it crucial for feature engineering in machine learning models.
Task
Implement a function solution(prices, d, threshold) that perfor
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