Forecast Combination by Using Artificial Neural Networks

One of the efficient ways for obtaining accurate forecasts is usage of forecast combination method. This approach consists of combining different forecast values obtained from different forecasting models. Also artificial neural networks and fuzzy time series approaches have proved their success in the field of forecasting. In this study, a new forecast combination approach based on artificial neural networks is proposed. The forecasts obtain from different fuzzy time series models are combined by utilizing artificial neural networks. The proposed method is applied to index of Istanbul stock exchange (IMKB) time series and the results are compared to other forecast combination methods available in the literature. As a result of the implementation, it is seen that the proposed forecast combination approach produces better forecasts than those produced by other methods.     

Forecasting model selection through out-of-sample rolling horizon weighted errors

Demand forecasting is an essential process for any firm whether it is a supplier, manufacturer or retailer. A large number of research works about time series forecast techniques exists in the literature, and there are many time series forecasting tools. In many cases, however, selecting the best time series forecasting model for each time series to be dealt with is still a complex problem. In this paper, a new automatic selection procedure of time series forecasting models is proposed. The selection criterion has been tested using the set of monthly time series of the M3 Competition and two basic forecasting models obtaining interesting results. This selection criterion has been implemented in a forecasting expert system and applied to a real case, a firm that produces steel products for construction, which automatically performs monthly forecasts on tens of thousands of time series. As result, the firm has increased the level of success in its demand forecasts.     

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.     

An experimental comparison of exponential and adaptive smoothing forecasting models using actual operating data

The forecast accuracy of exponential and adaptive smoothing models is compared using actual operating data. The data, involving the historical demand for twelve different medical supply items, was obtained from a large medical center. The adaptive smoothing model proposed by Trigg and Leach has been extended in this paper to include the continous adjustment of the smoothing constant values for the trend and seasonal factors in the forecasting model. The results indicate that while an adaptive smoothing model can reduce the bias in the forecasts in comparison with those produced by an exponential smoothing model, the adaptive smoothing model does not provide a significant reduction in the variation in the forecast errors (measured in terms of MAD) for most of the medical supply items studied.     

CMBCF: A Cloud Model Based Hybrid Method for Combining Forecast

Forecast combination has been proved to be an effective way to improve the forecasting accuracy. Most of the combining forecast methods now available belong to performance based weighting strategies, which judge the individual models mainly on the basis of their in-sample forecasting accuracy. Less attention has been paid to consider the characteristics underlying the distribution or the shape of forecasts from individual forecasters. However, information hidden in the distributions is of great value because the difference of shapes indicating distinct response towards the same pattern of a certain time series. In this paper, a cloud model based hybrid method for combining forecast(CMBCF) is proposed. In general, the new framework attempts to extract the local distribution characteristics of forecasting series by transforming the series into several cloud models. After the similarity comparison of the series represented in the form of cloud models, CMBCF assigns dynamic weights to individual models and construct the final combining forecast. The experimental results based on widely used time series data sets demonstrate the advantage of CMBCF over several traditional and state-of-art combining forecast strategies.     

一种基于数据融合和方法融合的时空综合预测算法

时空数据挖掘是数据挖掘中的重要研究内容,其中时空预测的应用领域最为广泛．针对目前时空预测方法中的不足,提出了一种基于数据融合和方法融合的时空综合预测算法．该方法首先采用统计学原理对目标对象本身的时序进行预测;然后通过神经网络解算相邻对象的空间影响,继而对混合数据序列使用时空自回归预测模型;最后使用线性回归将单个的时间预测、空间预测和时空预测有效地融合在一起,得到综合预测结果．应用该方法预测铁路客流,突破了传统铁路客流预测方法的局限,实验结果表明了算法的有效性．     

Probabilistic runoff volume forecasting in risk-based optimization for RTC of urban drainage systems

This article demonstrates the incorporation of stochastic grey-box models for urban runoff forecasting into a full-scale, system-wide control setup where setpoints are dynamically optimized considering forecast uncertainty and sensitivity of overflow locations in order to reduce combined sewer overflow risk. The stochastic control framework and the performance of the runoff forecasting models are tested in a case study in Copenhagen (76 km² with 6 sub-catchments and 7 control points) using 2-h radar rainfall forecasts and inlet flows to control points computed from a variety of noisy/oscillating in-sewer measurements. Radar rainfall forecasts as model inputs yield considerably lower runoff forecast skills than “perfect” gauge-based rainfall observations (ex-post hindcasting). Nevertheless, the stochastic grey-box models clearly outperform benchmark forecast models based on exponential smoothing. Simulations demonstrate notable improvements of the control efficiency when considering forecast information and additionally when considering forecast uncertainty, compared with optimization based on current basin fillings only.     

Combining Global Antithetic Forecasts

This paper provides a summary of the theory for antithetic forecasting, and an empirical exposition. An original forecast is combined with another forecast, produced from a time series which is antithetic (negatively correlated) to the original time series. The forecasts are combined via a linear projection of the antithetic series on the original series, such that the component forecasts have negatively correlated errors. Large-scale empirical tests and benchmark comparisons demonstrate the effectiveness of combining antithetic forecasts, even as these data depart from the strict theoretical lognormality requirement of antithetic forecasting. The method is illustrated in detail, using a real time series. Antithetic forecasting is the first combining method in which the gain increases with the forecast horizon.     

基于K-means和改进KNN算法的风电功率短期预测系统

为提高风电功率短期预测的准确性,针对KNN(K-Nearest neighbor algorithm)算法在风电功率预测中的不足,提出了基于K-means和改进KNN算法的风电功率短期预测方法;利用K-means聚类方法确定风电历史样本的类别,对KNN算法中搜索相似历史样本集的方式进行了改进和优化,构建了预测模型,并采用C/S架构实现了预测系统的设计;该系统具有自修正功能,能够随着预测次数的增加,不断修正预测模型,逐渐降低预测的误差率;以吉林省某风电场历史数据为样本进行了仿真分析,结果显示该算法与其它算法相比平均绝对误差和均方根误差最大下降1.08%和0.48%,运算时间提升了5.45%,在风电功率超短期多步预测中具有推广应用价值。     

Time-delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in India

Agricultural price forecasting is one of the challenging areas of time series forecasting. The feed-forward time-delay neural network (TDNN) is one of the promising and potential methods for time series prediction. However, empirical evaluations of TDNN with autoregressive integrated moving average (ARIMA) model often yield mixed results in terms of the superiority in forecasting performance. In this paper, the price forecasting capabilities of TDNN model, which can model nonlinear relationship, are compared with ARIMA model using monthly wholesale price series of oilseed crops traded in different markets in India. Most earlier studies of forecast accuracy for TDNN versus ARIMA do not consider pretesting for nonlinearity. This study shows that the nonlinearity test of price series provides reliable guide to post-sample forecast accuracy for neural network model. The TDNN model in general provides better forecast accuracy in terms of conventional root mean square error values as compared to ARIMA model for nonlinear patterns. The study also reveals that the neural network models have clear advantage over linear models for predicting the direction of monthly price change for different series. Such direction of change forecasts is particularly important in economics for capturing the business cycle movements relating to the turning points.     

A linear hybrid methodology for improving accuracy of time series forecasting

Modeling and forecasting of time series data are integral parts of many scientific and engineering applications. Increasing precision of the performed forecasts is highly desirable but a difficult task, facing a number of mathematical as well as decision-making challenges. This paper presents a novel approach for linearly combining multiple models in order to improve time series forecasting accuracy. Our approach is based on the assumption that each future observation of a time series is a linear combination of the arithmetic mean and median of the forecasts from all participated models together with a random noise. The proposed ensemble is constructed with five different forecasting models and is tested on six real-world time series. Obtained results demonstrate that the forecasting accuracies are significantly improved through our combination mechanism. A nonparametric statistical analysis is also carried out to show the superior forecasting performances of the proposed ensemble scheme over the individual models as well as a number of other forecast combination techniques.     

基于径向基神经网络的Web Service QoS#br# 属性值组合预测方法

为正确预测Web Service的服务质量(Quality of Service, QoS),帮助用户选择符合服务质量需求的Web Service,提出一种基于径向基神经网络模型的服务质量组合预测方法。首先使用时间序列模型对数据集建立线性和非线性预测模型,并选择最优模型,同时根据数据特点建立不同滑动窗口的灰色等维新息模型,再将上述2模型的预测结果作为输入源传递给径向基神经网络的训练模型,进行预测。实验结果表明,该方法与已有方法相比较,在预测精度方面有一定程度的提高。     

Optimal combination forecasts for hierarchical time series

In many applications, there are multiple time series that are hierarchically organized and can be aggregated at several different levels in groups based on products, geography or some other features. We call these “hierarchical time series”. They are commonly forecast using either a “bottom-up” or a “top-down” method.In this paper we propose a new approach to hierarchical forecasting which provides optimal forecasts that are better than forecasts produced by either a top-down or a bottom-up approach. Our method is based on independently forecasting all series at all levels of the hierarchy and then using a regression model to optimally combine and reconcile these forecasts. The resulting revised forecasts add up appropriately across the hierarchy, are unbiased and have minimum variance amongst all combination forecasts under some simple assumptions.We show in a simulation study that our method performs well compared to the top-down approach and the bottom-up method. We demonstrate our proposed method by forecasting Australian tourism demand where the data are disaggregated by purpose of travel and geographical region.     

基于层次聚类和极限学习机的母线短期负荷预测

利用传统方法预测母线负荷时,通常选取离待测日相近的一段时间作为历史相似日进行模型训练,没有考虑其天气情况、星期类型、节假日等因素的影响,相似日与待测日特征相差较大。为解决以上问题,提出一种基于层次聚类（HC）和极限学习机（ELM）的母线负荷预测算法。首先使用层次聚类法将母线历史日负荷进行聚类,然后对层次聚类得出的聚类结果建立决策树,其次根据待测日的温度、湿度、星期和节假日类型等日属性在决策树中匹配出训练极限学习机预测模型的历史日负荷,最后建立极限学习机预测模型,对待测日母线日负荷进行预测。对两条不同母线的负荷进行了预测,与传统单一的极限学习机相比,所提算法的平均绝对百分比误差（MAPE）分别降低了1.4和0.8个百分点。实验结果表明,所提算法预测母线负荷具有更高的预测精度和稳定性。     

基于ARIMA和LSTM混合模型的时间序列预测

由于现实中的时间序列通常同时具有线性和非线性特征,传统ARIMA模型在时间序列建模中常表现出一定局限性.对此,提出基于ARIMA和LSTM混合模型进行时间序列预测.应用线性ARIMA模型进行时间序列预测,用支持向量回归(SVR)模型对误差序列进行预测,采用深度LSTM模型对ARIMA模型和SVR模型的预测结果组合,并将...     

区间计量方法及其在油价预测中的应用研究

经典的计量经济学建模与预测方法是基于点数据的,忽略了区间内价格波动的大量信息,因而预测效果欠佳。引入区间计算与区间计量方法,应用于国际原油期货价格的预测,研究结果表明：相对于经典AR-GARCH模型的置信区间预测结果,区间计量方法的预测结果具有更高的准确度与更小的预测误差。研究证实了区间计算与区间计量方法的优越性,并揭示了在经济领域的重要应用价值。     

支持向量机在航空运输量预测中的应用

分析了现有航空运量预测方法存在的问题,建立了航空旅客运输量预测的支持向量回归模型,以中国南方航空公司1978～2002年的旅客运输量和相关指标的历史统计数据作为学习样本,分别选用不同的核函数,通过拟合训练进行预测,验证了支持向量机用于航空旅客运输量预测的有效性,并对模型中的有关参数进行了探讨分析。     

On deterministic chaos in software reliability growth models

Software reliability growth models attempt to forecast the future reliability of a software system, based on observations of the historical occurrences of failures. This allows management to estimate the failure rate of the system in field use, and to set release criteria based on these forecasts. However, the current software reliability growth models have never proven to be accurate enough for widespread industry use. One possible reason is that the model forms themselves may not accurately capture the underlying process of fault injection in software; it has been suggested that fault injection is better modeled as a chaotic process rather than a random one. This possibility, while intriguing, has not yet been evaluated in large-scale, modern software reliability growth datasets.We report on an analysis of four software reliability growth datasets, including ones drawn from the Android and Mozilla open-source software communities. These are the four largest software reliability growth datasets we are aware of in the public domain, ranging from 1200 to over 86,000 observations. We employ the methods of nonlinear time series analysis to test for chaotic behavior in these time series; we find that three of the four do show evidence of such behavior (specifically, a multifractal attractor). Finally, we compare a deterministic time series forecasting algorithm against a statistical one on both datasets, to evaluate whether exploiting the apparent chaotic behavior might lead to more accurate reliability forecasts.     

A combination of hidden Markov model and fuzzy model for stock market forecasting

This paper presents a novel combination of the hidden Markov model (HMM) and the fuzzy models for forecasting stock market data. In a previous study we used an HMM to identify similar data patterns from the historical data and then used a weighted average to generate a ‘one-day-ahead’ forecast. This paper uses a similar approach to identify data patterns by using the HMM and then uses fuzzy logic to obtain a forecast value. The HMM's log-likelihood for each of the input data vectors is used to partition the dataspace. Each of the divided dataspaces is then used to generate a fuzzy rule. The fuzzy model developed from this approach is tested on stock market data drawn from different sectors. Experimental results clearly show an improved forecasting accuracy compared to other forecasting models such as, ARIMA, artificial neural network (ANN) and another HMM-based forecasting model.     

Dynamics of firm financial evolution and bankruptcy prediction

The optimal forecasting horizon of bankruptcy prediction models is usually one year. Beyond this point, their accuracy decreases as the horizon recedes. However, the ability of models to provide good mid-term forecasts is an essential characteristic for financial institutions due to prudential reasons. This is why we have studied a method of improving their forecasts up to a 5-year horizon. For this purpose, we propose to quantize how firm financial health changes over time, typify these changes and design models that fit each type. Our results show that, whatever the modeling technique used to design prediction models, model accuracy can be significantly improved when the horizon exceeds two years. They also show that when our method is used in combination with ensemble-based models, model accuracy is always improved whatever the forecasting horizon, when compared to traditional models used by financial institutions. The method we propose in this article appears to be a reliable solution that makes it possible to solve a real problem most models are unable to overcome, and it can therefore help financial companies comply with the current recommendations made by the Basel Committee on Banking Supervision. It also provides the scientific community (which is interested in designing reliable failure models) with insights about how the evolution of firms’ financial situations over time can be modeled and efficiently used to make forecasts.