Applications of time-series analysis in accounting: A review

This paper reviews research that makes use of one of the most popular forecasting methods applied in accounting: time-series analysis using the Box-Jenkins methodology. It organizes the research in the area, surveys recent applications of time-series analysis in accounting, and discusses the potential for the methodology in addressing future research issues. The emphasis is on those aspects of the accounting system that possibly cause difficulties in applying time-series methods in accounting.     

Accuracy in forecasting: A survey

In this study, the author provides a brief and concise summary of empirical investigations pertaining to forecasting with special reference to the accuracy of different forecasting techniques. The study mainly focuses on comparisons of the accuracy of these techniques. The comparisons cover both quantitative and qualitative methods. In addition the summary includes studies seeking to test or improve accuracy by combining forecasting techniques.     

Time-series analysis supported by power transformations

This paper presents some procedures aimed at helping an applied time-series analyst in the use of power transformations. Two methods are proposed for selecting a variance-stabilizing transformation and another for bias-reduction of the forecast in the original scale. Since these methods are essentially model-independent, they can be employed with practically any type of time-series model. Some comparisons are made with other methods currently available and it is shown that those proposed here are either easier to apply or are more general, with a performance similar to or better than other competing procedures.     

基于小基线集时序干涉雷达分析武汉中心城区地表沉降时空特征

利用2015—2016年期间获取的15景Sentinel-1A TOPS SAR影像,采用小基线集时序干涉雷达(interferometric syn-thetic aperture radar,InSAR)分析方法,研究了武汉中心城区的地表沉降特征,分析并建立了该地区地表沉降与城市建设、降雨量和长江水位变化等因素的关...     

Integrating time-series and end-use methods to forecast electricity sales

Two types of forecasting methods have been receiving increasing attention by electric utility forecasters. The first type, called end-use forecasting, is recognized as an approach which is well suited for forecasting during periods characterized by technological change. The method is straightforward. The stock levels of energy-consuming equipment are forecast, as well as the energy consumption characteristics of the equipment. The final forecast is the product of the stock and usage characteristics. This approach is well suited to forecasting long time periods when technological change, equipment depletion and replacement, and other structural changes are evident. For time periods of shorter duration, these factors are static and variations are more likely to result from shocks to the environment. The shocks influence the usage of the equipment. A second forecasting approach using time-series analysis has been demonstrated to be superior for these applications. This paper discusses the integration of the two methods into a unified system. The result is a time-series model whose parameter effects become dynamic in character. An example of the models being used at the Georgia Power Company is presented. It is demonstrated that a time-series model which incorporates end-use stock and usage information is superior—even in short-term forecasting situations—to a similar time-series model which excludes the information.     

博山城区地下水开采资源评价和管理对策

博山是一个老工矿区,地表水已严重污染,地下水是唯一的供水水源。因过量开采,地下水位持续地大幅度下降。因此必须限制地下水的开采量。本文中利用时间序列方法确定地下水的允许开采量,得到如下的多元回归方程△h_t=27.919-0.113817P_(t-1) 4.9451Q_(TOTt) 2.4329QL_t 式中:△h_t为当年和上一年的最低水位差值(米);P_(t-1)为上一年的总降水量(毫米);Q_(TOTt)为城区当年的总开采量(万立方米每天):QL_t为良庄水源地的开采量(万立方米每天)。在该回归方程的基础上建立了地下水资源的管理图解,见正文图6。根据上一年的降水量可确定当年的允许开采量。例如上一年降水677毫米时,当年的开采量平均为8.58万立方米每天,可使地下水位维持在上一年的水平。另一方面可用人工回灌的方法增地加下水资源。孝妇河流经岩溶区时,沿河有8个以上的大漏水点,如在该地筑坝拦蓄洪水,可使地下水资源增加20—30％。在本文中还建议当河流经过灰岩区时要作防渗处理以免污染地下水。     

A Simple Additive Weighting Method for Time-SeriesMultiindices Decision Making and Its Applications

1.INTRODUCTIONThecomprehensiveevaluationofeconomicbenefitsisoftenencowteredintheeconomicresearch.Anditisesselltiallyamultiindicesdecision-makingproblem,whichalsomaybecalledmultiattributedecisionmaking,multicriteriadecisionmaking,multiobjectivedecisionmakingandsoon.Butinpractice,wealsowanttoassess,compare,rankandanalyzetheco~ehensiveeconomicbenefitoveraperiod.Thiskindofeconomicbenefitisreferredtoasoveralleconomicbenefit.Theevaluationofoveralleconomicbellefitisactuallyatimeseriesmultiindice…     

Forecasting car expenditures using household survey data

Surveys collecting data on consumer attitudes and buying intentions have been performed in Sweden since 1973. This paper examines the usefulness of these data as quick indicators of the development of household expenditures on automobiles. In the evaluation we are considering the explanatory power as well as the prediction accuracy. It turns out that the best single indicator is among the plan indices. However, an indicator based on car registration statistics is found to be at least as good. By combining plan/attitude indices with car registrations our study shows that considerable improvements can be obtained.     

Diagnostic checks of non-standard time series models

Diagnostic checks have become a standard tool for helping to assess the adequacy of a forecasting system since Box and Jenkins' (1970) ARIMA modelling technique became popular. However, most of the research has developed checks for normal or second-order stationary models. This paper gives various diagnostic checks that can be performed simply on nonnormal, non-standard models such as the class of multiprocess models (Harrison and Stevens, 1976), where residuals are definitely not normal. The performance to date of these models can then be objectively scrutinized on-line. Examples, including a generalized cusum technique, are given to illustrate the effectiveness of the techniques on specific series.