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Title: 適應性學習模型應用於銅價預測
An adaptive learning-based model for copper price forecasting
Authors: 楊仁瀚
Yang, Ren-Han
Contributors: 林怡伶

Lin, Yi-Ling
Tsaih, Rua-Huan

Yang, Ren-Han
Keywords: 自適應單隱藏層前饋神經網路
Adaptive single-hidden layer feed-forward neural network
Concept drift
Copper price forecasting
Moving window
Structural change
Date: 2022
Issue Date: 2022-02-10 12:54:02 (UTC+8)
Abstract: 銅在工業生產過程中扮演著不可或缺的工業原料之一,其價格變動的掌握對於相關的工業計劃與參與者來說至關重要。由於銅價的波動型態經常隨著時間推移而有所變化,往往會造成開發出的預測模型無法有效因應。為了因應銅價的變動特性,在本篇研究中除了檢驗出銅價具有結構性變化的特性並提出適應性學習型預測模型 (ALFM) 在動態變化的環境中學習。因爲結構性轉變在文獻中被證實與概念飄移在本質上存在著相近概念,所以本研究所提出之預測模型中除了加入移動窗口機制來因應銅價背後所存在的概念飄移與結構性轉變,並於自適應單隱藏層前饋神經網路 (ASLFN) 中設計序列型學習 (SS) 機制,以因應類神經網絡在學習具有複雜擬合函數資料時常面臨到梯度消失與擬合過度之問題。
由於 SS 機制是本研究中首次提出,因此其有效性有必要被加以驗證,我們使用長江有色金屬網的銅現貨價進行實驗。實驗結果除了驗證 ALFM 中 SS 機制是有效的之外,即 SS 機制當中的模組安排皆為必要,同時 SS 機制也被證實可以有效解決自適應單隱藏層前饋神經網路所遭遇梯度消失與擬合過度之問題。在所提出的預測模型中移動窗口機制與 SS 機制皆有助於提高預測能力,這使得所提出的 ALFM 比文獻中的其他工具有更好的預測結果,而且訓練時間是可以被接受的。最後,在與文獻中所使用的工具(如:SARIMA、SLFN、SVR、RNN、LSTM 以及 GRU)相比後,可以發現 ALFM 具有更好的預測結果。
An accurate forecasting model for the price volatility of copper plays a vital role in decision-making for industrial projects and related companies. The challenge to deploy models is the change of the data over time, which commonly leads to significant mispredictions. In this paper, the structural change in copper prices has been examined. The adaptive learning-based forecasting model (ALFM) is proposed to learn the patterns under a dynamic changing environment, which combines the moving window mechanism and sequentially structuring (SS) mechanism. The moving window mechanism is used to address the concept drift and structural change behind the copper price. The sequentially structuring (SS) mechanism is designed for the adaptive single hidden layer feed-forward neural network (ASLFN) in response to solving the vanishing gradient and overfitting problems.
The SS mechanism is first proposed in this study and thus should be validated. We use the copper spot prices of Yangtze River (YR) nonferrous metals as application data. The experiment results provide evidence for examining the arrangement of SS mechanism does work in the training process. The proposed ideas of these modules within the SS mechanism can cope with the vanishing gradient or alleviate the overfitting tendency. Furthermore, both the moving window mechanism and SS mechanism in the proposed forecasting model help to improve the prediction ability, which makes the ALFM have better prediction results than other tools in the literature, and the training time is acceptable. The baseline models are seasonal ARIMA model (SARIMA), single-hidden layer feedforward neural network (SLFN), support vector regression (SVR), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU).
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