本研究受到Zeng, Chen, Zhang, & Xu (2023)發現單層線性模型在長期時間序列預測（LTSF）中出乎意料的有效性啟發，該模型在多變量預測任務中的表現超越了現有的基於Transformer的模型。考慮到黃金的獨特性及其作為一個獨立資產類別的地位，本研究選擇黃金價格作為研究樣本。我們關注黃金價格預測中面臨的非穩態學習挑戰——概念漂移，並探索使用移動窗口機制搭配單隱藏層前饋神經網絡（SLFN）作為一種類似單層線性模型的結構較簡單的神經網絡模型來解決此問題。為了克服模型訓練過程中遇到的梯度消失和過擬合問題，我們提出了IOSFCR機制來調整SLFN模型裡面的隱藏節點數量以增強模型的適應性和預測能力，並將此SLFN模型命名為自適應單隱藏層前饋神經網路（Adaptive SLFN）模型。本研究旨在評估IOSFCR機制對於訓練Adaptive SLFN模型的有效性，並比較其預測結果與當前在預測時間序列的領域上最先進的Transformer模型，FEDformer的性能。

This study is inspired by the findings of Zeng, Chen, Zhang, & Xu (2023), which highlighted the unexpected efficacy of single-layer linear models in long-term time series forecasting (LTSF), outperforming existing Transformer-based models in multivariate forecasting tasks. Given gold's unique properties and its status as a distinct asset class, this research selects gold prices as the sample. We address the non-stationary learning challenge of concept drift in forecasting gold prices and explore the use of a moving window mechanism combined with a single-hidden layer feedforward neural network (SLFN) as a simpler neural network model, akin to a single-layer linear model, to solve this issue. To overcome the challenges of vanishing gradient and overfitting encountered during model training, we introduce the IOSFCR mechanism to adjust the number of hidden nodes within the SLFN model to enhance the model's adaptability and forecasting capability, and we name this enhanced SLFN model as the adaptive single-hidden layer feedforward neural network (Adaptive SLFN) model. The aim of this study is to assess the effectiveness of the IOSFCR mechanism in training the Adaptive SLFN model and to compare its forecasting performance against the current state-of-the-art Transformer model in the realm of time series forecasting, FEDformer.

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