預測問題在過去的研究當中引起了很多人的研究興趣，並且在過去的研究當中指出在預測研究裡，機器學習的技術比傳統統計的技術有更好的表現。支持向量機是一種機器學習的技術，並且當有最佳的核參數時，用此技術去執行預測有不錯的預測正確性。支持向量機是一組分析數據和識別模式的學習方法，用於分類和回歸分析。在過去的研究中，支持向量機有其較高的計算負擔解決有限制條件的優化問題，而最小二乘支持向量機以解決線性方程組來代替解決支持向量機的二次規劃問題。因此，最小二乘支持向量機的計算負擔比支持向量機來的小。由於最小二乘支持向量機的參數優化的重要性，實係數的基因演算法和粒子群演算法被用來優化最小二乘支持向量機裡的核參數和權衡參數。實係數的基因演算法使用實值參數為染色體來減少計算過程。粒子群演算法是一個隨機優化技術，基於鳥群的移動和食物情報來得到問題的最佳解。為了決定出支持向量機的最佳的參數，此研究分別比較了兩種演化演算法在預測正確性和求出最佳的核參數的效率這兩方面。本論文以實係數的基因演算法和粒子群演算法為基礎的最小二乘支持向量機方法來預測市場趨勢。在這項研究中預測了三個通訊產業例子的發展趨勢。本論文引用了手機市場預測和寬頻使用戶的趨勢來驗證所提出的方法的預測準確性。最後的預測結果是令人滿意的，實證研究結果裡的預測誤差可被壓縮在5%以下。在未來論文提出的預測方法可應用在其他產業的市場增長(趨勢)預測。

The forecasting problems have evoked a lot of research interests in the past. Recently, studies have demonstrated that machine learning techniques can achieve better performance than tradi- tional statistical methods. The support vector machine (SVM) is one kind of machine learning techniques with good forecasting accuracy when with optimal kernel parameters. The SVMs are a set of related supervised learning methods that analyze data and recognize patterns, used for clas- sification and regression analysis. In past research, SVM had its higher computational burden for the constrained optimization programming and least squares support vector machine (LS-SVM) solved linear equations instead of a quadratic programming problem. Therefore, comparison of SVM, LS-SVM have smaller computational burden. Due to the importance of parameters opti- mization in LS-SVM model, the real valued genetic algorithm (RGA) and the particle swarm op- timization (PSO) were used to optimize the kernel parameter and tradeoff parameter in LS-SVM. The RGA uses a real value as a parameter of the chromosome in populations to reduce the comput- ing process. The PSO is a stochastic optimization technique which is inspired by social behavior of bird flocking and fish schooling. To determine the optimal parameters in a LS-SVM, this study proposed two novel evolutionary algorithms, the RGA and the PSO based LS-SVM, for enhancing forecasting accuracy and the efficiency of obtaining the kernel parameters. The two methods will further be verified by predicting the market trends of communication industries. The forecasts of three market trends in the communication industry are forecasting in the empirical study. The pro- posed RGA based LS-SVM and PSO based LS-SVM were verified by using the mobile phone and broadband technology market growth forecasting. The forecast efficiency is satisfactory; all fore- casting errors in the empirical study results can be compressed to fewer than 5%. In the future, the GA and PSO based LS-SVM can further be applied on other forecasts of industry market growth or trends.

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