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Seasonal fluctuation interference often affects the relational analysis of economic time series. The main purpose of this paper is to propose a new grey relational model for relational analysis of seasonal time series and apply it to identify and eliminate the influence of seasonal fluctuation of retail sales of consumer goods in China.

First, the whole quarterly time series is divided into four groups by data grouping method. Each group only contains the time series data in the same quarter. Then, the new series of four-quarters are used to establish the grey correlation model and calculate its correlation coefficient. Finally, the correlation degree of factors in each group of data was calculated and sorted to determine its importance.

The data grouping method can effectively reflect the correlation between time series in different quarters and eliminate the influence of seasonal fluctuation.

In this paper, the main factors influencing the quarterly fluctuations of retail sales of consumer goods in China are explored by using the grouped grey correlation model. The results show that the main factors are different from quarter to quarter: in the first quarter, the main factors are money supply, tax and per capita disposable income of rural residents. In the second quarter are money supply, fiscal expenditure and tax. In the third quarter are money supply, fiscal expenditure and per capita disposable income of rural residents. In the fourth quarter are money supply, fiscal expenditure and tax.

This paper successfully realizes the application of grey relational model in quarterly time series and extends the applicable scope of grey relational model.

Analyzing medicare data is a role undertaken by the government and commercial companies for accepting the appeals and sanctioning the claims of those insured under Medicare. As the data of medicare is robust and made up of heterogeneous typed columns, traditional approaches consist of a laborious and time-consuming process. The understanding and processing of such data sets and finding the role of each attribute for data analysis are tricky tasks which this research will attempt to ease. The paper aims to discuss these issues.

This paper proposes a Hierarchical Grouping (HG) with an experimental model to handle the complex data and analysis of the categorical data which consist of heterogeneous typed columns. The HG methodology starts with feature subset selection. HG forms a structure by quantitatively estimating the similarities and forms groups of the features for data. This is carried by applying metrics like decomposition; it splits the dataset and helps to analyze thoroughly under different labels with different selected attributes of Medicare data. The method of fixed regression includes metrics of re-indexing and grouping which works well for multiple keys (multi-index) of categorical data. The final stage of structure is applying multiple aggregation function on each attribute for quantitative computation.

The data are analyzed quantitatively with the HG mechanism. The results shown in this paper took less computation cost and speed, which are usually incurred on the publicly available data sets.

The motive of this paper is to provide a supportive work for the tasks like outlier detection, prediction, decision making and prescriptive tasks for multi-dimensional data.

It provides a new efficient approach to analyze medicare data sets.

Recently the advances in wireless communication technology and the popularity of portable computers have rendered mobile computing environments from which mobile users with battery‐operated palmtops can access the information via wireless channels, without space and time restriction. In mobile computing environments, mobile users cache the data items to use the bandwidth efficiently and improve the response time of mobile transactions. If the data items cached in mobile users are updated at the server, the server broadcasts an invalidation report for maintaining the cache consistency of mobile users. However, this method has an obstacle that does not guarantee the serializable execution of mobile transactions. The purpose of this paper is to propose the four types of reports for mobile transaction (FTR‐MT) method for ensuring the serializable execution of mobile transactions.

The paper describes the FTR‐MT method, which is composed of four types of algorithms, e.g. group report composition algorithm, immediate commit decision algorithm, cache consistency algorithm, and disconnection cache consistency algorithm. FTR‐MT method for improving the response time of mobile transactions makes a commit decision by using the four types of reports.

With the FTR‐MT method, mobile users can make a commit decision by using the four types of reports. The response time of mobile transactions can be reduced. Furthermore, the FTR‐MT method can improve the cache efficiency in the case that the disconnection of mobile users is longer than the broadcast interval of the window report.

This paper proposes a new method for guaranteeing the serializable execution of mobile transactions, called FTR‐MT, using four types of reports. Also, it can prevent the entire cache dropping, even though the disconnection of a mobile host is longer than the broadcast interval of a window report. Through the analytical model, this method is felt to be superior to other methods, in terms of the average response time and the commit rate of mobile transactions, and bandwidth usage.

We provide an overview of how artificial intelligence is transforming the identification, structuring, and prioritization of customer needs – known as the voice of the customer (VOC). First, we summarize how the VOC helps firms gain insights on using user-generated data. Second, we discuss the types of user-generated data and the challenges associated with analyzing each type of data. Third, we describe common methods, matched to the firms' goals and the structure of the data, that are used to analyze the VOC. Fourth, and most importantly, we map the methods to relevant applications, providing guidance to select the appropriate method to address the desired research questions.

Changes in food retailing (globalization, concentration) have negative impacts on smaller, “traditional” food retail businesses. Their market share decreasing year by year. The purpose of this study is to examine and compare the financial performances of these businesses under the given circumstances and current economic environment in a Hungarian and a Romanian county.

The study is based on two complete databases, including all companies that behoove retail food activity (considering the NACE cod) in the counties of Hajdu-Bihar (Hungary) and Cluj (Romania). The database analyzed contains the financial statements for five consecutive years for 212 and 690 businesses. Databases were examined by the most typical financial indicators using the multivariate and univariate analysis of variance and the k-medoid cluster analysis methods.

The results of the analysis have shown that there are differences in the number of retail food companies in the case of two counties, both in number and in financial performance. Companies in Hajdú-Bihar county perform better in terms of financial ratios than those in Cluj county. The groups created by k-medoids cluster analysis are relatively well distinguished in the case of Hajdú-Bihar county, while the picture is much more mixed in the case of Kolozs county. However, it is also important to note that the companies analyzed should generally perform better to survive.

Among the limitations of the study, it is important to note that the findings are relevant only to the two counties examined. Another limiting factor is that quite several companies had to be excluded from the analysis due to missing data or outliers.

The study presents for the corporate decision-makers the current performance of the companies of the sector examined in the two counties. The results of the study highlight the business areas of concern in management. The findings show that they need to change this performance to strengthen their market position. We believe that it is not enough to complain about the expansion of the supermarket chains, but they should take appropriate actions to improve their situation. Based on the results of the study, it can be concluded that there is a need to improve the financial efficiency of retail food companies in both counties to survive in the long run. This improvement is essential because retailers can play an important role in smaller settlements and narrower residential environments.

Comparative analysis of retail food companies in similar counties in these two neighboring countries has not been conducted using complex financial analysis. The study revealed the common and/or individual characteristics of these companies.

The research objective is to increase the computational efficiency of the automated teller machine (ATM) cash demand forecasting problem. It proposes a practical decision-making process that uses aggregated time series of a bank's ATM network. The purpose is to decrease ATM numbers that will be forecasted by individual models, by finding the machines’ cluster where the forecasting results of the aggregated series are appropriate to use.

A comparative statistical forecasting approach is proposed in order to reduce the calculation complexity of an ATM network by using the NN5 competition data set. Integrated autoregressive moving average (ARIMA) and its seasonal version SARIMA are fitted to each time series. Then, averaged time series are introduced to simplify the forecasting process carried out for each ATM. The ATMs that are forecastable with the averaged series are identified by calculating the forecasting accuracy change in each machine.

The proposed approach is evaluated by different error metrics and is compared to the literature findings. The results show that the ATMs that have tolerable accuracy loss may be considered as a cluster and can be forecasted with a single model based on the aggregated series.

The research is based on the public data set. Financial institutions do not prefer to share their ATM transactions data, therefore accessible data are limited.

The proposed practical approach will be beneficial for financial institutions to use, that hold an excessive number of ATMs because it reduces the computational time and resources allocated for the forecasting process.

This study offers an effective simplified methodology to the challenging cash demand forecasting process by introducing an aggregated time series approach.

The aim of the present article is to decide the ideal number of warehouses for a food manufacturer in the north of Spain (Asturias) for the year 2000, and their ideal location in the Spanish Peninsula by cluster analysis. The stages followed are to comment first on the underlying assumptions of the study, then on the methodology and the structure of the program developed to solve the problem, as well as on their input and output files. How the cluster and cost are calculated is discussed and finally the solution to this real case is provided.

Purpose — In order to analyse applicability, comparability and limitations of GPS technology in travel surveys, different mobility survey techniques were tested in an Austrian pilot study.

Methodology/approach — Four groups of voluntary respondents recorded their travel behaviour over a time period of three consecutive days. The groups were assigned to three different and combined methods of data collection: Paper–pencil trip diaries, passive GPS tracking, active GPS tracking and prompted recall interviews.

Findings — The resulting mobility parameters show that self-reported paper– pencil surveys yield accurate sociodemographic information on the respondents as well as trip purposes and modes of transportation, although too few trips are reported. Passive GPS-based methods minimize the strain for respondents. Methods that combine GPS-based data collection and questionnaire provide the most reliable mobility data at the moment.

Research limitations/implications — Due to funding restrictions the sample sizes had to be relatively small (235 participants). Further development in research methodology will increase the effectiveness of automated data analysis, for example more accurate detection of activities and transport modes. The usefulness of GPS-based data collection in a large-scale surveys is planned to be tested in the next Austrian national travel survey.

Originality/value of paper — The pilot study allows a detailed comparison of traditional and GPS-based travel survey methods for the first time, due to data collection combined with prompted recalls.

We write this chapter using a historical discourse, both in the chronology of research and in the development that has occurred over the years with curriculum-based measurement (CBM). More practically, however, we depict the chronology in terms of the sequence of decisions that educators make as they provide special services to students with disabilities. In the first part of the chapter, we begin with a pair of seminal documents that were written in the late 1970s to begin the story of CBM. In the second part of the chapter, we begin with the first decision an educator needs to make in providing special services and then we continue through the chronology of decisions to affect change in learning for individual students. In the end, we conclude with the need to integrate these decisions with multiple references for interpreting data: normative to allocate resources, criterion to diagnose skill deficits, and individual to evaluate instruction.