A heuristic method for estimating time-series models for forecasting. I

The aim is to develop a heuristic method for estimating time-series models for forecasting. The study consists of two parts. This one presents the analytical framework of the proposed procedure; the second will present the actual algorithm and numerical evaluations of the process. Our approach makes use of the frequency-domain theory of second-order stochastic processes to remedy several of the problems that we encounter in fitting ARIMA-type models for forecasting. Within this framework some of the problems that the present study addresses are: sample size of time series, initial estimates of the coefficients, convergence of difficult data to stable estimates, and computing time.     

Electricity consumption prediction using a neural-network-based grey forecasting approach

Electricity consumption is an important economic index and plays a significant role in drawing up an energy development policy for each country. Multivariate techniques and time-series analysis have been proposed to deal with electricity consumption forecasting, but a large amount of historical data is required to obtain accurate predictions. The grey forecasting model attracted researchers by its ability to characterize an uncertain system effectively with a limited number of samples. GM(1,1) is the most frequently used grey forecasting model, but its developing coefficient and control variable were dependent on the background value that is not easy to be determined, whereas a neural-network-based GM(1,1) model called NNGM(1,1) has been presented to resolve this troublesome problem. This study has applied NNGM(1,1) to electricity consumption and has examined its forecasting ability on electricity consumption using sample data from the Turkish Ministry of Energy and Natural Resources and the Asia–Pacific Economic Cooperation energy database. Experimental results demonstrate that NNGM(1,1) performs well.     

我国固定资产投资的ARIMA模型分析与预测

本文综合运用了时间序列预测方法,对我国固定资产投资总额进行了分析,建立了自回归求积移动模型ARIMA（4,1,4）。检验结果表明,该模型提供了较好的顸测结果,可为我国全社会固定资产投资提供可靠的参考数据。     

Short-term forecasting of traffic delays in highway construction zones using on-line approximators

Highway construction zones are often the cause of traffic delays. This is a natural consequence of the high congestion and nonuniform traffic flow conditions in construction zones. Most of the current algorithms for computing traffic delays are accurate for low density traffic conditions, and address the estimation of current travel time only. This paper presents a method for short-term forecasting of traffic delays in highway construction zones using data from presence detectors. The method is based on a modular approach wherein data from adjacent detectors is processed for estimating the travel time between the two detectors. The travel time estimates are then considered as time-series data, and the problem of short-term forecasting of traffic delay is formulated as a time-series evolution problem. A generic structure referred to as an on-line approximator is used for the prediction of travel time based on current and past travel time estimates. Simulation examples are used to illustrate the traffic delay forecasting algorithm.     

基于组合模型的武隆景区长期游客流量预测

由于某景区经营权回购需进行景区游客量长期预测。为克服长期预测的不确定性,我们采用基于旅游地环境容量的LOGISTIC模型和考虑客源地旅游需求的回归迭代模型进行组合预测。前者包含旅游地的环境容量限制参数,主要基于供给制约因素结合时间序列数据进行预测。后者主要从客源地的人均收入、价格水平、价格弹性和收入弹性进行预测。然后整合两模型进行组合预测,对两种预测结果进行加权得到组合预测游客流量,很好地解决了集成旅游地环境容量、"申遗"前后与高速公路通车前后游客量变化、游客时间序列规律、客源地人均可支配收入、旅游地吸引力和旅游地生命周期等众多因素进行长期预测的问题。预测结果作为政府部门经营权回购中补偿额确定的主要依据,已被采用。     

Handling forecasting problems using fuzzy time series

In [6–9], Song et al. proposed fuzzy time-series models to deal with forecasting problems. In [10], Sullivan and Woodall reviewed the first-order time-invariant fuzzy time series model and the first-order time-variant model proposed by Song and Chissom [6–8], where the models are compared with each other and with a time-invariant Markov model using linguistic labels with probability distributions. In this paper, we propose a new method to forecast university enrollments, where the historical enrollments of the University of Alabama shown in [7,8] are used to illustrate the forecasting process. The average forecasting errors and the time complexity of these methods are compared. The proposed method is more efficient than the ones presented in [7, 8, 10] due to the fact that the proposed method simplifies the arithmetic operation process. Furthermore, the average forecasting error of the proposed method is smaller than the ones presented in [2, 7, 8].     

Adaptive neural network model for time-series forecasting

In this study, a novel adaptive neural network (ADNN) with the adaptive metrics of inputs and a new mechanism for admixture of outputs is proposed for time-series prediction. The adaptive metrics of inputs can solve the problems of amplitude changing and trend determination, and avoid the over-fitting of networks. The new mechanism for admixture of outputs can adjust forecasting results by the relative error and make them more accurate. The proposed ADNN method can predict periodical time-series with a complicated structure. The experimental results show that the proposed model outperforms the auto-regression (AR), artificial neural network (ANN), and adaptive k-nearest neighbors (AKN) models. The ADNN model is proved to benefit from the merits of the ANN and the AKN through its’ novel structure with high robustness particularly for both chaotic and real time-series predictions.     

ARMA模型在哈尔滨气温预测中的应用

将时间序列分析引入到气温时间序列预测的研究中,深入分析气温样本数据,并对其建立ARMA模型.采用最佳准则函数法确定模型的阶数,并利用自相关函数对模型的残差进行了检验.通过条件期望预测和适时修正预测方法求得预测值,与真实值的比较得到适时修正预测精确度比条件期望预测的精确度高.     

Recursive estimation: A unified approach to the identification estimation,and forecasting of hydrological systems

Mathematical models of hydrological and water-resource systems have been formulated in many different ways and with various levels of complexity. There are advantages to be gained, therefore, by trying to unify some of the more common models within a statistical framework which will allow for more objective methods of model calibration. In this paper, we consider the general class of linear, dynamic models, as applied to the characterisation of flow and dispersion behavior in rivers, and show how these can be unified within the context of recursive time-series analysis and estimation. This allows not only for more objective, data-based approaches to stochastic model structure identification, but also for improved statistical estimation and the development of both constant parameter and self-adaptive, Kalman-filter-based forecasting procedures. The unified approach presented in the paper is being applied successfully in other environmental areas, such as soil science, climatic data analysis, meterological forecasting, and plant physiology.     

A novel procedure for multimodel development using the grey silhouette coefficient for small-data-set forecasting

Small-data-set forecasting problems are a critical issue in various fields, with the early stage of a manufacturing system being a good example. Manufacturers require sufficient knowledge to minimize overall production costs, but this is difficult to achieve due to limited number of samples available at such times. This research was thus conducted to develop a modelling procedure to assist managers or decision makers in acquiring stable prediction results from small data sets. The proposed method is a two-stage procedure. First, we assessed some single models to determine whether the tendency of a real sequence can be reflected using grey incidence analysis, and we then evaluated their forecasting stability based on the relative ratio of error range. Second, a grey silhouette coefficient was developed to create an applicable hybrid forecasting model for small samples. Two real cases were analysed to confirm the effectiveness and practical value of the proposed method. The empirical results showed that the multimodel procedure can minimize forecasting errors and improve forecasting results with limited data. Consequently, the proposed procedure is considered a feasible tool for small-data-set forecasting problems.     

An extended grey forecasting model for omnidirectional forecasting considering data gap difference

To achieve effective and efficient decision making in a highly competitive business environment, an enterprise must have an appropriate forecasting technique that can meet the requirements of both timeliness and accuracy. Accordingly, in the early stages, building a forecasting model with incomplete information and limited samples is very important to a business. Grey system theory is one of the prediction methods that can be built with a small sample and yet has a strong ability to make short-term predictions. The purpose of this study is to come up with an improved forecasting model based on the concept of this theory to enlarge the applicability of the grey forecasting model in various situations. By extending the data transforming approach, this method generalizes a building procedure for the grey model to grasp the data outline and information trend. Specifically, a novel inverse accumulating generation operator is developed to enable omnidirectional forecasting. The research utilizes observations of the titanium alloy fatigue limit along with temperature changes as raw data to verify the performance of the proposed method. The experimental results show that not only can this method expand the application scope of the grey forecasting model, but also improve its forecasting accuracy.     

Some evidence on forecasting time-series with support vector machines

The importance of predicting future values of a time-series transcends a range of disciplines. Economic and business time-series are typically characterized by trend, cycle, seasonal, and random components. Powerful methods have been developed to capture these components by specifying and estimating statistical models. These methods include exponential smoothing, autoregressive integrated moving average (ARIMA), and partially adaptive estimated ARIMA models. New research in pattern recognition through machine learning offers innovative methodologies that can improve forecasting performance. This paper presents a study of the comparative results of time-series analysis on nine problem domains, each of which exhibits differing time-series characteristics. Comparative analyses use ARIMA selection employing an intelligent agent, ARIMA estimation through partially adaptive methods, and support vector machines. The results find that support vector machines weakly dominate the other methods and achieve the best results in eight of nine different data sets.     

基于海南省旅游人数的数学建模

使用两种不同的建模方法,研究了海南省2002～2009年的月度旅游人数,并建立了相应的数学模型.方法一利用微分方程结合传统时间序列分解法和自回归分布滞后模型;方法二利用ARIMA(p,d,q)(P,D,Q)~s模型.通过比较两种不同的方法,最终选出最优预测模型.     

Forecast combinations for intermittent demand

Intermittent demand is characterised by infrequent demand arrivals, where many periods have zero demand, coupled with varied demand sizes. The dual source of variation renders forecasting for intermittent demand a very challenging task. Many researchers have focused on the development of specialised methods for intermittent demand. However, apart from a case study on hierarchical forecasting, the effects of combining, which is a standard practice for regular demand, have not been investigated. This paper empirically explores the efficiency of forecast combinations in the intermittent demand context. We examine both method and temporal combinations of forecasts. The first are based on combinations of different methods on the same time series, while the latter use combinations of forecasts produced on different views of the time series, based on temporal aggregation. Temporal combinations of single or multiple methods are investigated, leading to a new time-series classification, which leads to model selection and combination. Results suggest that appropriate combinations lead to improved forecasting performance over single methods, as well as simplifying the forecasting process by limiting the need for manual selection of methods or hyper-parameters of good performing benchmarks. This has direct implications for intermittent demand forecasting in practice.     

模糊时间数列的分析与预测:以台湾地区加权股价指数为例

随着我国经济快速成长，衍生性金融商品的投资分析，已成为国内财务数学研究热门课题。以股票市场而言，人们总希望比别人早一步掌握行情的脉动，以获取最高的报酬率，然而，影响股市加权股价指数波动的因素众多，要如何进行趋势分析与预测，是很多学者相当感兴趣与研究的主题。本文考虑以模糊统计方法，作模糊时间数列的趋势分析与预测。其望应用模糊统计分析方法比传统的时间数列分析方法能得到更合理的解释，且预测结果可以提供决策者更多的信息，做出正确的决策。最后以台湾地区加权股票指数为例，做一实证上的详细探讨。     

Forecasting accuracy of behavioural models for participation in the arts

This paper assesses the forecasting performance of count data models applied to arts attendance. We estimate participation models for two artistic activities that differ in their degree of popularity – museums and jazz concerts – with data derived from the 2002 release of the Survey of Public Participation in the Arts for the United States. We estimate a finite mixture model – a zero-inflated negative binomial model – that allows us to distinguish between “true” non-attendants and “goers” and their respective behaviour regarding participation in the arts. We evaluate the predictive (in-sample) and forecasting (out-of-sample) accuracy of the estimated model using bootstrapping techniques to compute the Brier score. Overall, the results indicate the model performs well in terms of forecasting. Finally, we draw certain policy implications from the model’s forecasting capacity, thereby allowing the identification of target populations.     

银行间国债利率期限结构的三因子仿射模型

通过对2002年1月到2006年3月的国内银行间国债数据的主成份分析表明，利率的动态变化基本上可以被三个因子所解释。由此我们建立了一个三因子仿射模型，并给出债券的定价公式。通过卡尔曼滤波法对模型的实证分析，证实该模型基本上能在时间序列和横截面两个维度上与实际数据相符合。从预测能力来看，0．5年和1年期利率的预测结果相对误差略大，而2、3、5、10年期利率的相对误差较小，平均值相对误差在1％之内。     

A grey modeling procedure based on the data smoothing index for short-term manufacturing demand forecast

Product life cycles have become increasingly shorter because of global competition. Under fierce competition, the use of small samples to establish demand forecasting models is crucial for enterprises. However, limited samples typically cannot provide sufficient information; therefore, this presents a major challenge to managers who must determine demand development trends. To overcome this problem, this paper proposes a modified grey forecasting model, called DSI–GM(1,1). Specifically, we developed a data smoothing index to analyze the data behavior and rewrite the calculation equation of the background value in the applied grey modeling, constructing a suitable model for superior forecasting performance according to data characteristics. Employing a test on monthly demand data of thin film transistor liquid crystal display panels and the monthly average price of aluminum for cash buyers, the proposed modeling procedure resulted in high prediction outcomes; therefore, it is an appropriate tool for forecasting short-term demand with small samples.     

Synthesis or selection of forecasting models

This report is relevant to the practical forecasting situation in which a decision-maker is faced with several feasible predictors for his variable of interest. If he has a substantial amount of data available on the performance of each of his predictors, then it is well known that a composite forecast can be suitably derived as an optimal forecasting procedure. Alternatively, if only a small amount of evidence is available on the predictors' performance, then there appear to be controversial recommendations upon whether it is still optimal to pursue a policy of synthesis leading to a composite predictor or whether it is better to attempt a selection of the singularly best forecasting model. This report discusses some of the associated issues and provides some experimental evidence on the performance of these two policies.     

On modelling and forecasting of vector stochastic processes

Some deterministic and multivariate stochastic model identification techniques for setting up a suitable dynamic model for an observed multivariate process are described. Results of a case study involving the problem of multinodal load forecasting are presented in order to illustrate the practical utility of the methods discussed.