A hybrid modeling approach for forecasting the volatility of S&P 500 index return

Forecasting volatility is an essential step in many financial decision makings. GARCH family of models has been extensively used in finance and economics, particularly for estimating volatility. The motivation of this study is to enhance the ability of GARCH models in forecasting the return volatility. We propose two hybrid models based on EGARCH and Artificial Neural Networks to forecast the volatility of S&P 500 index. The estimates of volatility obtained by an EGARCH model are fed forward to a Neural Network. The input to the first hybrid model is complemented by historical values of other explanatory variables. The second hybrid model takes as inputs both series of the simulated data and explanatory variables. The forecasts obtained by each of those hybrid models have been compared with those of EGARCH model in terms of closeness to the realized volatility. The computational results demonstrate that the second hybrid model provides better volatility forecasts.     

基于离散灰色预测模型与人工神经网络混合智能模型的时尚销售预测

时尚销售预测对零售领域十分重要,准确的销售情况预测有助于大幅度提高最终时尚销售利润。针对目前时尚销售预测数据量有限并且数据波动大导致难以进行准确预测的问题,提出了一种结合人工神经网络（ANN）算法和离散灰色预测模型（DGM（1,1））算法的混合智能预测算法。该算法通过关联度分析得到关联度大的影响变量,在利用DGM（1,1）+ANN预测之后,引入二次残差的思想,将实际销售数据与DGM（1,1）+ANN预测结果的残差加入影响变量利用ANN进行第二次残差预测。最后通过真实的时尚销售数据验证算法预测的可行性及准确性。实验结果表明,该算法在时尚销售数据的预测中,预测平均绝对百分误差（MAPE）在25%左右,预测性能优于自回归积分滑动平均模型（ARIMA）、扩展极限学习机（EELM）、DGM（1,1）、DGM（1,1）+ANN算法,相较于以上几种算法平均预测精度大约提高8个百分点。所提混合智能算法可用于时尚销售即时预测,且能够大幅度提高销售的效益。     

Data-driven models for monthly streamflow time series prediction

Data-driven techniques such as Auto-Regressive Moving Average (ARMA), K-Nearest-Neighbors (KNN), and Artificial Neural Networks (ANN), are widely applied to hydrologic time series prediction. This paper investigates different data-driven models to determine the optimal approach of predicting monthly streamflow time series. Four sets of data from different locations of People’s Republic of China (Xiangjiaba, Cuntan, Manwan, and Danjiangkou) are applied for the investigation process. Correlation integral and False Nearest Neighbors (FNN) are first employed for Phase Space Reconstruction (PSR). Four models, ARMA, ANN, KNN, and Phase Space Reconstruction-based Artificial Neural Networks (ANN-PSR) are then compared by one-month-ahead forecast using Cuntan and Danjiangkou data. The KNN model performs the best among the four models, but only exhibits weak superiority to ARMA. Further analysis demonstrates that a low correlation between model inputs and outputs could be the main reason to restrict the power of ANN. A Moving Average Artificial Neural Networks (MA-ANN), using the moving average of streamflow series as inputs, is also proposed in this study. The results show that the MA-ANN has a significant improvement on the forecast accuracy compared with the original four models. This is mainly due to the improvement of correlation between inputs and outputs depending on the moving average operation. The optimal memory lengths of the moving average were three and six for Cuntan and Danjiangkou, respectively, when the optimal model inputs are recognized as the previous twelve months.     

Volatility of main metals forecasted by a hybrid ANN-GARCH model with regressors

In this article, we analyze volatility forecasts associated with the price of gold, silver, and copper, three of the most important metals in the world market. First, a group of GARCH models are used to forecast volatility, including explanatory variables like the US Dollar-Euro and US Dollar-Yen exchange rates, the oil price, and the Chinese, Indian, British, and American stock market indexes. Subsequently, these model predictions are used as inputs for a neural network in order to analyze the increase in hybrid predictive power. The results obtained show that for these three metals, using the hybrid neural network model increases the forecasting power of out-of-sample volatility. In order to optimize the results, we conducted a series of sensitizations of the artificial neural network architecture and analyses for different cases, finding that the best models to forecast the price return volatility of these main metals are the ANN-GARCH model with regressors. Due to the heteroscedasticity in the financial series, the loss function used is Heteroskedasticity-adjusted Mean Squared Error (HMSE), and to test the superiority of the models, the Model Confidence Set is used.     

A comparative study of artificial neural networks,and decision trees for digital game content stocks price prediction

Precise prediction of stock prices is difficult chiefly because of the many intervening factors. Unpredictability is particularly notable in the aftermath of the global financial crisis. Data mining may however be used to discover highly correlated estimation models. This study looks at artificial neural networks (ANN), decision trees and the hybrid model of ANN and decision trees (hybrid model), the three common algorithm methods used for numerical analysis, to forecast stock prices. The author compared the stock price forecasting models derived from the three methods, and applied the models on 10 different stocks in 320 data sets in an empirical forecast. Average accuracy of ANN is 15.31%, the highest, in terms of match with real market stock prices, followed by decision trees, at 14.06%; hybrid model is 13.75%. The study also discovers that compared to the other two methods, ANN is a more stable method for predicting stock prices in the volatile post-crisis stock market.     

A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data

A suitable combination of linear and nonlinear models provides a more accurate prediction model than an individual linear or nonlinear model for forecasting time series data originating from various applications. The linear autoregressive integrated moving average (ARIMA) and nonlinear artificial neural network (ANN) models are explored in this paper to devise a new hybrid ARIMA–ANN model for the prediction of time series data. Many of the hybrid ARIMA–ANN models which exist in the literature apply an ARIMA model to given time series data, consider the error between the original and the ARIMA-predicted data as a nonlinear component, and model it using an ANN in different ways. Though these models give predictions with higher accuracy than the individual models, there is scope for further improvement in the accuracy if the nature of the given time series is taken into account before applying the models. In the work described in this paper, the nature of volatility was explored using a moving-average filter, and then an ARIMA and an ANN model were suitably applied. Using a simulated data set and experimental data sets such as sunspot data, electricity price data, and stock market data, the proposed hybrid ARIMA–ANN model was applied along with individual ARIMA and ANN models and some existing hybrid ARIMA–ANN models. The results obtained from all of these data sets show that for both one-step-ahead and multistep-ahead forecasts, the proposed hybrid model has higher prediction accuracy.     

Improvement of time forecasting models using a novel hybridization of bootstrap and double bootstrap artificial neural networks

Hybrid models such as the Artificial Neural Network-Autoregressive Integrated Moving Average (ANN–ARIMA) model are widely used in forecasting. However, inaccuracies and inefficiency remain in evidence. To yield the ANN–ARIMA with a higher degree of accuracy, efficiency and precision, the bootstrap and the double bootstrap methods are commonly used as alternative methods through the reconstruction of an ANN–ARIMA standard error. Unfortunately, these methods have not been applied in time series-based forecasting models. The aims of this study are twofold. First, is to propose the hybridization of bootstrap model and that of double bootstrap mode called Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (B-ANN–ARIMA) and Double Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (DB-ANN–ARIMA), respectively. Second, is to investigate the performance of these proposed models by comparing them with ARIMA, ANN and ANN–ARIMA. Our investigation is based on three well-known real datasets, i.e., Wolf’s sunspot data, Canadian lynx data and, Malaysia ringgit/United States dollar exchange rate data. Statistical analysis on SSE, MSE, RMSE, MAE, MAPE and VAF is then conducted to verify that the proposed models are better than previous ARIMA, ANN and ANN–ARIMA models. The empirical results show that, compared with ARIMA, ANNs and ANN–ARIMA models, the proposed models generate smaller values of SSE, MSE, RMSE, MAE, MAPE and VAF for both training and testing datasets. In other words, the proposed models are better than those that we compare with. Their forecasting values are closer to the actual values. Thus, we conclude that the proposed models can be used to generate better forecasting values with higher degree of accuracy, efficiency and, precision in forecasting time series results becomes a priority.     

Customer churn prediction by hybrid neural networks

As churn management is a major task for companies to retain valuable customers, the ability to predict customer churn is necessary. In literature, neural networks have shown their applicability to churn prediction. On the other hand, hybrid data mining techniques by combining two or more techniques have been proved to provide better performances than many single techniques over a number of different domain problems. This paper considers two hybrid models by combining two different neural network techniques for churn prediction, which are back-propagation artificial neural networks (ANN) and self-organizing maps (SOM). The hybrid models are ANN combined with ANN (ANN + ANN) and SOM combined with ANN (SOM + ANN). In particular, the first technique of the two hybrid models performs the data reduction task by filtering out unrepresentative training data. Then, the outputs as representative data are used to create the prediction model based on the second technique. To evaluate the performance of these models, three different kinds of testing sets are considered. They are the general testing set and two fuzzy testing sets based on the filtered out data by the first technique of the two hybrid models, i.e. ANN and SOM, respectively. The experimental results show that the two hybrid models outperform the single neural network baseline model in terms of prediction accuracy and Types I and II errors over the three kinds of testing sets. In addition, the ANN + ANN hybrid model significantly performs better than the SOM + ANN hybrid model and the ANN baseline model.     

基于KNN-ANN算法的边际电价预测

在电力市场中,价格一直受到买卖双方的广泛关注。但是,电价影响因素的不确定性给电价的预测带来难度。针对该问题,提出一种通过结合人工神经网络和KNN算法来进行时间序列预测的模型,用KNN算法找出历史数据中相似的数据子序列集合(最近邻),并用人工神经网络来寻找这些最近邻的最优权重,得出预测的时间序列。以美国纽约州电力市场的电价数据进行实验分析,同时比较了利用ARIMA算法以及Naive I预测的结果,证明该方法简单、有效。     

An adaptive forecasting approach for copper price volatility through hybrid and non-hybrid models

This article studies monthly volatility forecasting for the copper market, which is of practical interest for various participants such as producers, consumers, governments, and investors.Using data from 1990 to 2016, we propose a framework composed of a set of time series models such as Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), non-parametric models from soft computing, e.g. Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS), and hybrid specifications of both. The adaptability characteristic of these models in exogenous variables, their configuration parameters and window size, simultaneously, are provided by a Genetic Algorithm in pursuit of achieving the best possible forecasts. Also, recognized drivers of this specific market are considered.We examine out-of-sample performance based on Heteroskedasticity-adjusted Mean Squared Error (HMSE), and we test model superiority using the Model Confidence Set (MCS). The results show that making forecasts using an adaptive technique is crucial to obtaining robust and improved performance. The Adaptive-GARCH–FIS specification yielded the best forecasting power.     

三江源区不同土壤类型有机质含量高光谱反演

近年来高光谱遥感技术被广泛运用于土壤有机质含量反演的研究中。基于三江源区玉树县和玛多县采集的146个土壤样品的室内ASD FieldSpec 4实测光谱数据及4种变换形式,利用偏最小二乘回归(PLSR)和人工神经网络(ANN)建立土壤有机质含量高光谱预测模型。结果表明:ANN模型反演土壤有机质含量的整体精度高于PLSR模型,总均方根误差均在17.51以下;但是,不同土壤类型的最佳反演模型及指标却有所差异:高山草甸土和沼泽土的最佳反演模型和指标均为ANN模型和BD指标,模型总均方根误差分别为10.29和3.29;高山草原土的最佳反演模型是PLSR模型,最佳指标是REF指标,模型总均方根误差为5.59;山地草甸土的最佳反演模型为〖JP2〗PLSR模型,最佳指标为BD指标,模型总均方根误差为4.68。研究发现,利用ANN模型和PLSR模型都能较好地预测三江源区4种土壤类型的有机质含量,而波段深度则是该区域的最佳反演指标。〖JP〗
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基于BP网的中长期负荷预测因素优化选择

基于人工神经网络原理,设计一个由输入层、隐含层和输出层组成的三层BP网络模型,利用神经网络高度非线性建模能力,选取影响电力负荷的一些经济因素作为BP人工神经网络的输入变量,采用新定义的方差贡献法对输入变量进行优化选择,对预测精度的影响进行探讨。仿真结果证明,采用方差贡献法对影响中长期电力负荷预测的相关因素进行优化选择是可行有效的。     

Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures

In this research, we work with data of futures contracts on foreign exchange rates for British pound (BP), Canadian dollar (CD), and Japanese yen (JY) that are traded at the Chicago Mercantile Exchange (CME) against US dollars. We model relationships between exchange rates in these currencies using linear models, feed forward artificial neural networks (ANN), and three versions of recurrent neural networks (RNN1, RNN2 and RNN3) for predicting exchange rates in these currencies against the US dollar. Our results on forecast evaluations based on AGS test the tests of forecast equivalence between any two competing models among the entire models employed for each of the series show that ANN and the three versions of RNN models offer superior forecasts for predicting BP, CD and JY exchange rates although the forecast evaluations based on MGN test are in sharp contrast. On the other hand forecast based on SIGN test shows that ANN and all the versions of RNN models offer superior forecasts for BP and CD in exception of JY exchange rates. The results for forecast evaluation for all the models for each of the series based on summary measures of forecast evaluations show that RNN3 model appears to offer the most accurate predictions of BP and RNN1 for JP exchange rates. However, none of the RNN models appear to be statistically superior to the benchmark (i.e., linear model) for predicting CD exchange rates.      

基于混合贝叶斯SVM的电价分类与预测

电价的分类与预测是电力市场电价理论研究中的重要内容。该文提出了混合贝叶斯支持向量机方法(BE-SVM)，通过贝叶斯统计方法对电价进行分类，挖掘有效的数据信息，并结合支持向量机(SVM)技术预测现货电价数据，贝叶斯前验分布和后验分布用来估计SVM中的参数。通过比较模型BE-SVM、SVM 和神经网络(ANN)的预测结果，表明该文提出的BE-SVM方法提高了电价的预测精度，是一种有效的方法。     

Stream flow predictions using nature-inspired Firefly Algorithms and a Multiple Model strategy – Directions of innovation towards next generation practices

Stream flow prediction is studied by Artificial Intelligence (AI) in this paper using Artificial Neural Network (ANN) as a hybrid of Multi-Layer Perceptron (MLP) with the Levenberg–Marquardt (LM) backpropagation learning algorithm (MLP-LM) and (ii) MLP integrated with the Fire-Fly Algorithm (MLP-FFA). Monthly stream flow records used in this prediction problem comprise two stations at Bear River, the U.S.A., for the period of 1961–2012. Six different model structures are investigated for both MLP-LM and MLP-FFA models and their results were analysed using a number of performance measures including Correlation Coefficients (CC) and the Taylor diagram. The results indicate a significant improvement is likely in predicting downstream flows by MLP-FFA over that by MLP-LM, attributed to identifying the global minimum. In addition, an emerging multiple model (ensemble) strategy is employed to treat the outputs of the two MLP-LM and MLP-FFA models as inputs to an ANN model. The results show yet another further possible improvement. These two avenues for improvements identify possible directions towards next generation research activities.     

Bayesian testing for non-linearity in volatility modeling

Neural networks provide a tool for describing non-linearity in volatility processes of financial data and help to answer the question “how much” non-linearity is present in the data. Non-linearity is studied under three different specifications of the conditional distribution: Gaussian, Student-t and mixture of Gaussians. To rank the volatility models, a Bayesian framework is adopted to perform a Bayesian model selection within the different classes of models. In the empirical analysis, the return series of the Dow Jones Industrial Average index, FTSE 100 and NIKKEI 225 indices over a period of 16 years are studied. The results show different behavior across the three markets. In general, if a statistical model accounts for non-normality and explains most of the fat tails in the conditional distribution, then there is less need for complex non-linear specifications.     

周相似特性下交通流组合预测方法研究

根据交通流量具有周相似的特性,构造了周相似序列。用霍特指数平滑法对周相似序列进行预测,用人工神经网络对残差部分进行预测。将指数平滑法与神经网络法相结合,以便发挥每种方法的优势,获得比单个方法更好的预测结果。实例分析表明,比单独使用ARIMA或单独使用神经网络方法,使用组合方法的预测误差最小,适合于实时的交通流预测。     

Forecasting financial time series volatility using Particle Swarm Optimization trained Quantile Regression Neural Network

Accurate forecasting of volatility from financial time series is paramount in financial decision making. This paper presents a novel, Particle Swarm Optimization (PSO)-trained Quantile Regression Neural Network namely PSOQRNN, to forecast volatility from financial time series. We compared the effectiveness of PSOQRNN with that of the traditional volatility forecasting models, i.e., Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and three Artificial Neural Networks (ANNs) including Multi-Layer Perceptron (MLP), General Regression Neural Network (GRNN), Group Method of Data Handling (GMDH), Random Forest (RF) and two Quantile Regression (QR)-based hybrids including Quantile Regression Neural Network (QRNN) and Quantile Regression Random Forest (QRRF). The results indicate that the proposed PSOQRNN outperformed these models in terms of Mean Squared Error (MSE), on a majority of the eight financial time series including exchange rates of USD versus JPY, GBP, EUR and INR, Gold Price, Crude Oil Price, Standard and Poor 500 (S&P 500) Stock Index and NSE India Stock Index considered here. It was corroborated by the Diebold–Mariano test of statistical significance. It also performed well in terms of other important measures such as Directional Change Statistic (Dstat) and Theil's Inequality Coefficient. The superior performance of PSOQRNN can be attributed to the role played by PSO in obtaining the better solutions. Therefore, we conclude that the proposed PSOQRNN can be used as a viable alternative in forecasting volatility.     

An optimized ANN model based on genetic algorithm for predicting ripping production

Due to the environmental constraints and the limitations on blasting, ripping as a ground loosening and breaking method has become more popular in both mining and civil engineering applications. As a result, a more applicable rippability model is required to predict ripping production (Q) before conducting such tests. In this research, a hybrid genetic algorithm (GA) optimized by artificial neural network (ANN) was developed to predict ripping production results obtained from three sites in Johor state, Malaysia. It should be noted that the mentioned hybrid model was first time applied in this field. In this regard, 74 ripping tests were investigated in the studied areas and the relevant parameters were also measured. A series of GA–ANN models were conducted in order to propose a hybrid model with a higher accuracy level. To demonstrate the performance capacity of the hybrid GA–ANN model, a pre-developed ANN model was also proposed and results of predictive models were compared using several performance indices. The results revealed higher accuracy of the proposed hybrid GA–ANN model in estimating Q compared to ANN technique. As an example, root-mean-square error values of 0.092 and 0.131 for testing datasets of GA–ANN and ANN techniques, respectively, express the superiority of the newly developed model in predicting ripping production.

     

A hybrid artificial neural network—mechanistic model for centrifugal compressor

A mathematical model is an important tool for design and optimization of centrifugal compressor. However, owing to the varying compressor speeds and the complexity of the flow dynamics inside the impeller and diffuser, the currently available mechanistic models may yield inaccurate results. The purpose of this paper is to present a hybrid modeling approach for developing a quantitatively accurate model for centrifugal compressor. Two novel hybrid models, that is, additive and multiplicative hybrid models each of which consists of a three-layer back-propagation artificial neural network (ANN) component and a mechanistic component suitably modified to describe the performances of multistage centrifugal compressor, were constructed and compared with the well-developed ANN model. The results from the hybrid models showed better performance compared to the ANN model. Besides, the hybrid models demonstrated much better performance than the pure mechanistic model, and the multiplicative hybrid model, in general, showed better accuracy than that of the additive hybrid model in our case.