标题： 基于离散小波变换的股票指数预测：对印度国家证券交易所五十指数的研究 显示英文标题

标题： Discrete Wavelet Transform-Based Prediction of Stock Index: A Study on National Stock Exchange Fifty Index

摘要： 金融时间序列，如股票价格和汇率，通常是非线性和非平稳的。 分解模型的使用已被发现可以提高预测模型的准确性。 本文提出了一种混合方法，结合了分解模型（即最大重叠离散小波变换（MODWT））和机器学习模型（ANN 和 SVR）的优点，以预测国家证券交易所五十指数。 在第一阶段，数据通过 MODWT 被分解成较少数量的子序列。 在下一阶段，每个子序列使用机器学习模型（即 ANN 和 SVR）进行预测。 预测的子序列被聚合以获得最终的预测结果。 在最后阶段，使用误差度量和统计检验来评估所提出方法的有效性。 所提出的方法（MODWT-ANN 和 MODWT-SVR）与 ANN 和 SVR 模型进行了比较，结果发现，基于 MODWT-SVR 模型预测值的交易规则所获得的投资回报率高于买入并持有策略。 显示英文摘要

摘要： Financial Times Series such as stock price and exchange rates are, often, non-linear and non-stationary. Use of decomposition models has been found to improve the accuracy of predictive models. The paper proposes a hybrid approach integrating the advantages of both decomposition model (namely, Maximal Overlap Discrete Wavelet Transform (MODWT)) and machine learning models (ANN and SVR) to predict the National Stock Exchange Fifty Index. In first phase, the data is decomposed into a smaller number of subseries using MODWT. In next phase, each subseries is predicted using machine learning models (i.e., ANN and SVR). The predicted subseries are aggregated to obtain the final forecasts. In final stage, the effectiveness of the proposed approach is evaluated using error measures and statistical test. The proposed methods (MODWT-ANN and MODWT-SVR) are compared with ANN and SVR models and, it was observed that the return on investment obtained based on trading rules using predicted values of MODWT-SVR model was higher than that of Buy-and-hold strategy.