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Difficulty: Medium
Category: time_series
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Topics: time-series, exponential-smoothing, optimization, forecasting
Simple Exponential Smoothing (SES) is a foundational model for one-step-ahead forecasting of mean-reverting time series. In quantitative finance, it is used for applications like intraday alpha research and adaptive execution algorithms. This problem involves finding an optimal smoothing parameter by minimizing in-sample forecast errors, providing a data-driven approach to model calibration. Task Implement the function solution(series: list, h: int) -> list that determines the optimal Simple Ex
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