基于ARIMA与神经网络集成的GDP时间序列预测研究

本文深入分析了单整自回归移动平均(ARIMA)模型与神经网络(NN)模型的预测特性和优劣,并在此基础上建立了由ARIMA模型和NN模型集成的GDP时间序列预测模型与算法。其基本思想是充分发挥两种模型在线性空间和非线性空间的预测优势,据此将GDP时间序列的数据结构分解为线性自相关主体和非线性残差两部分,首先用ARIMA模型预测序列的线性主体,然后用NN模型对其非线性残差进行估计,最终集成为整个序列的预测结果。仿真实验表明:集成模型的预测准确率显著高于单一模型的预测准确率,从而证实了集成模型用于GDP预测的有效性。     

Forecasting urban traffic flow by SVR with continuous ACO

Accurate forecasting of inter-urban traffic flow has been one of the most important issues globally in the research on road traffic congestion. Because the information of inter-urban traffic presents a challenging situation, the traffic flow forecasting involves a rather complex nonlinear data pattern. In the recent years, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. This investigation presents a short-term traffic forecasting model which combines the support vector regression model with continuous ant colony optimization algorithms (SVRCACO) to forecast inter-urban traffic flow. Additionally, a numerical example of traffic flow values from northern Taiwan is employed to elucidate the forecasting performance of the proposed SVRCACO model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA) time series model. Therefore, the SVRCACO model is a promising alternative for forecasting traffic flow.     

主成分分析与支持向量机相结合的区域降水预测应用

将主成分分析和支持向量机回归相结合,以广西5、6月区域平均日降水量作为预报对象,进行区域日降水量预测研究.首先,整理分析大量的T213数值预报产品信息数据进行主成分分析,得到主成分数据序列;其次,根据主成分数据序列建立训练集训练支持向量机,并利用遗传算法优化参数;最后,输入支持向量机所需数据,得到主成分预测结果,建立广西日降水预报模型.实例计算结果表明,支持向量机回归模型比逐步回归模型有更好的预测能力.     

Neural network forecasting for seasonal and trend time series

Neural networks have been widely used as a promising method for time series forecasting. However, limited empirical studies on seasonal time series forecasting with neural networks yield mixed results. While some find that neural networks are able to model seasonality directly and prior deseasonalization is not necessary, others conclude just the opposite. In this paper, we investigate the issue of how to effectively model time series with both seasonal and trend patterns. In particular, we study the effectiveness of data preprocessing, including deseasonalization and detrending, on neural network modeling and forecasting performance. Both simulation and real data are examined and results are compared to those obtained from the Box–Jenkins seasonal autoregressive integrated moving average models. We find that neural networks are not able to capture seasonal or trend variations effectively with the unpreprocessed raw data and either detrending or deseasonalization can dramatically reduce forecasting errors. Moreover, a combined detrending and deseasonalization is found to be the most effective data preprocessing approach.     

Hybrid evolutionary algorithms in a SVR traffic flow forecasting model

Accurate urban traffic flow forecasting is critical to intelligent transportation system developments and implementations, thus, it has been one of the most important issues in the research on road traffic congestion. Due to complex nonlinear data pattern of the urban traffic flow, there are many kinds of traffic flow forecasting techniques in literature, thus, it is difficult to make a general conclusion which forecasting technique is superior to others. Recently, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. This investigation presents a SVR traffic flow forecasting model which employs the hybrid genetic algorithm-simulated annealing algorithm (GA-SA) to determine its suitable parameter combination. Additionally, a numerical example of traffic flow data from northern Taiwan is used to elucidate the forecasting performance of the proposed SVRGA-SA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA), back-propagation neural network (BPNN), Holt-Winters (HW) and seasonal Holt-Winters (SHW) models. Therefore, the SVRGA-SA model is a promising alternative for forecasting traffic flow.     

Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization

The motivation for this paper is to introduce a hybrid neural network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF–PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a neural network fitness function for financial forecasting purposes. This is done by benchmarking the ARBF–PSO results with those of three different neural networks architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD) plus a na?¨ve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time series over the period January 1999–March 2011 using the last 2 years for out-of-sample testing.     

A new method to mitigate data fluctuations for time series prediction

Although the classic exponential-smoothing models and grey prediction models have been widely used in time series forecasting, this paper shows that they are susceptible to fluctuations in samples. A new fractional bidirectional weakening buffer operator for time series prediction is proposed in this paper. This new operator can effectively reduce the negative impact of unavoidable sample fluctuations. It overcomes limitations of existing weakening buffer operators, and permits better control of fluctuations from the entire sample period. Due to its good performance in improving stability of the series smoothness, the new operator can better capture the real developing trend in raw data and improve forecast accuracy. The paper then proposes a novel methodology that combines the new bidirectional weakening buffer operator and the classic grey prediction model. Through a number of case studies, this method is compared with several classic models, such as the exponential smoothing model and the autoregressive integrated moving average model, etc. Values of three error measures show that the new method outperforms other methods, especially when there are data fluctuations near the forecasting horizon. The relative advantages of the new method on small sample predictions are further investigated. Results demonstrate that model based on the proposed fractional bidirectional weakening buffer operator has higher forecasting accuracy.     

Improved method of sea level forecasting at Venice (Northern Adriatic Sea)

In this paper a forecasting method for the extremely dangerous aqua alta phenomenon is developed. The city of Venice, which is located in the northeastern part of Italy, is often subjected to intense flooding, due to increasing of the sea level. The classical methods used in Geophysics generally failed in forecasting this phenomenon, because of the complexity of the physical modelling. Therefore, a method based on a non-linear auto-regressive moving average (ARMA) approach is proposed here. It is found that the time series, corresponding to the sea level data, present a Gaussian distribution, but during short periods, of order of few days, a strong non-Gaussian behaviour is evident, which is concomitant with aqua alta events. This suggests the development of a non-linear version of the usual ARMA models. An empirical orthogonal function technique is applied in order to avoid numerical pathologies of the model due to the particular characteristics of the time series. The encouraging results indicate that such technique is a good tool for forecasting and alarm purposes.     

An Examination of the Use of Adaptive Filtering in Forecasting

Adaptive filtering is a technique for preparing short- to medium-term forecasts based on the weighting of historical observations, in a similar way to moving average and exponential smoothing. However, adaptive filtering, as it has been developed in electrical engineering, attempts to distinguish a signal pattern from random noise, rather than simply smoothing the noise of past data. This paper reviews the technique of adaptive filtering and investigates its applications and limitations for the forecasting practitioner. This is done by looking at the performance of adaptive filtering in forecasting a number of time series and by comparing it with other forecasting techniques.     

时间序列分解预测法及周期因素的探讨

本文运用时间序列分解预测法，分析门诊人次的变动规律，预测了季度门诊人次。本法与移动平均比率预测法相比，预测精度提高３１％。本文还探讨了时间序列分解预测的周期因素等有关问题。     

Day‐ahead price forecasting based on hybrid prediction model

Short‐Term Price Forecast is a key issue for operation of both regulated power systems and electricity markets. Energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. However, this forecasting problem is complex due to nonlinear, nonstationary, and time variant behavior of electricity price time series. So, in this article, the forecast model includes wavelet transform, autoregressive integrated moving average, and radial basis function neural networks (RBFN) is presented. Also, an intelligent algorithm is applied to optimize the RBFN structure, which adapts it to the specified training set, reduce computational complexity and avoids over fitting. Effectiveness of the proposed method is applied for price forecasting of electricity market of mainland Spain and its results are compared with the results of several other price forecast methods. These comparisons confirm the validity of the developed approach. © 2016 Wiley Periodicals, Inc. Complexity 21: 156–164, 2016     

A new look at forecasting annual corporate earnings in the U.S.A.

In this study, composite earnings per share models are estimated for 35 chemical, food, and utility firms during the 1981–1982 period. Although it is generally held that financial analysts produce superior earnings forecasts when compared to time series model forecasts, the results of this study indicate that analysts fared very poorly in 1982 and the average mean square forecasting error of analyst forecasts may be reduced by 74.2 percent by combining analyst and univariate time series model forecasts. This reduction is very interesting when one finds that the univariate time series model forecasts do not substantially deviate from those produced by random walk drift models, the ARIMA (0, 1, 1) process. Moreover, despite the high degree of correlation existing among analyst and time series forecasts, the ordinary least squares estimation of the composite earnings model is a better forecasting model than the composite earnings models estimated with ridge regression and latent root regression techniques.     

Optimized prediction model for concrete dam displacement based on signal residual amendment

The traditional statistical model of concrete dam's displacement monitoring is used widely in hydraulic engineering. However, the forecasting precision of the conventional calculation model is poor due to the antiquated method of information mining and weak generalization capacity. Furthermore, the uncertain chaos effect implied in residual sequence is also intractable for modeling. In consideration of the nonlinearity, time variation, and unsteadiness of the chaotic characteristics of a dam time series, multiscale wavelet technology is used to decompose and reconstruct the residuals of multiple regression models. The fitting prediction of the low-frequency autocorrelation part is completed through the linear training ability of the autoregressive integrated moving average (ARIMA) model, and the support vector machine (SVM) regression model is constructed to optimize and process the nonlinear high-frequency signal. Then, a combined forecasting model for concrete dam's displacement based on signal residual amendment is established. The analysis of an engineering example indicates that the combined model built in this study can identify the time–frequency nonlinear characteristics of the prototype monitoring signal well, thus improving its fitting precision, antinoise ability, and robustness. In addition, the combined mathematical model established in this study is improved and developed for application to the prediction analysis of the effect quantities of other hydraulic structures.     

Using ARMA models to forecast workpiece roundness error in a turning operation

The paper describes the methodology for developing autoregressive moving average (ARMA) models to represent the workpiece roundness error in the machine taper turning process. The method employs a two stage approach in the determination of the AR and MA parameters of the ARMA model. It first calculates the parameters of the equivalent autoregressive model of the process, and then derives the AR and MA parameters of the ARMA model. Akaike's Information Criterion (AIC) is used to find the appropriate orders m and n of the AR and MA polynomials respectively. Recursive algorithms are developed for the on-line implementation on a laboratory turning machine. Evaluation of the effectiveness of using ARMA models in error forecasting is made using three time series obtained from the experimental machine. Analysis shows that ARMA(3,2) with forgetting factor of 0.95 gives acceptable results for this lathe turning machine.     

Maximum likelihood estimation for all-pass time series models

An autoregressive-moving average model in which all roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa is called an all-pass time series model. All-pass models generate uncorrelated (white noise) time series, but these series are not independent in the non-Gaussian case. An approximate likelihood for a causal all-pass model is given and used to establish asymptotic normality for maximum likelihood estimators under general conditions. Behavior of the estimators for finite samples is studied via simulation. A two-step procedure using all-pass models to identify and estimate noninvertible autoregressive-moving average models is developed and used in the deconvolution of a simulated water gun seismogram.     

Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization

In real time, one observation always relies on several observations. To improve the forecasting accuracy, all these observations can be incorporated in forecasting models. Therefore, in this study, we have intended to introduce a new Type-2 fuzzy time series model that can utilize more observations in forecasting. Later, this Type-2 model is enhanced by employing particle swarm optimization (PSO) technique. The main motive behind the utilization of the PSO with the Type-2 model is to adjust the lengths of intervals in the universe of discourse that are employed in forecasting, without increasing the number of intervals. The daily stock index price data set of SBI (State Bank of India) is used to evaluate the performance of the proposed model. The proposed model is also validated by forecasting the daily stock index price of Google. Our experimental results demonstrate the effectiveness and robustness of the proposed model in comparison with existing fuzzy time series models and conventional time series models.     

基于时间序列法的国税月度收入预测模型研究

研究了基于时间序列方法的国税月度收入预测. 通过采用Box-Jenkins的ARIMA模型, 结合国税月度收入数据, 分析并提出了一套针对月度税收收入的预测研究框架, 包括对税收预测模型的拟合、检验、预测、评价、动态修正等主要环节的处理方法. 在该研究框架的指导下, 以增值税、海关代征税和营业税为例, 对2006年各月的税收收入进行了模拟预测, 月度税收收入预测的平均相对误差分别控制在5.47\%, 8.63\%和2.37\%. 最后给出了在实际应用中动态修正税收预测模型的建议, 并简要讨论了时间序列方法在税收预测中面临的问题.     

岩土工程位移时间序列的组合预测研究

将组合预测方法用于岩土工程位移时间序列预测.结合实际观测数据,分别建立位移时间序列预测的GM(1,1)模型、Verhulst模型和趋势曲线模型.采用极小误差法确定各单一模型的权重,建立组合预测模型.应用表明,组合预测的精度高,为岩土工程位移预测提供了一种实用、可靠的方法.     

基于神经网络的期货预测数据预处理问题研究

期货预测研究在期货价格数据预处理和预测方法上存在直接套用原始数据代入模型以及价格预测模型和原始数据模型不相匹配等问题,需要予以解决.本研究在采用通货膨胀率指数调整、平均周期项以及滤波等方法对铜期货价格时间序列数据进行预处理后,分别将预处理前后的期货价格数据输入到神经网络预测模型,通过比较两者预测结果来验证原始期货时间序列数据预处理的必要性.     

Some properties of a simple moving average when applied to forecasting a time series

Simple (equally weighted) moving averages are frequently used to estimate the current level of a time series, with this value being projected as a forecast for future observations. A key measure of the effectiveness of the method is the sampling error of the estimator, which this paper defines in terms of characteristics of the data. This enables the optimal length of the average for any steady state model to be established and the lead time forecast error derived. A comparison of the performance of a simple moving average (SMA) with an exponentially weighted moving average (EWMA) is made. It is shown that, for a steady state model, the variance of the forecast error is typically less than 3% higher than the appropriate EWMA. This relatively small difference may explain the inconclusive results from the empirical studies about the relative predictive performance of the two methods.