Forecasting telecommunications data with linear models

For telecommunication companies to successfully manage their business, companies rely on mapping future trends and usage patterns. However, the evolution of telecommunications technology and systems in the provision of services renders imperfections in telecommunications data and impinges on a company’s’ ability to properly evaluate and plan their business. ITU Recommendation E.507 provides a selection of econometric models for forecasting these trends. However, no specific guidance is given. This paper evaluates whether simple extrapolation techniques in Recommendation E.507 can generate accurate forecasts. Standard forecast error statistics—mean absolute percentage error, median absolute percentage error and percentage better—show the ARIMA, Holt and Holt-D models provide better forecasts than a random walk and other linear extrapolation methods.linear models; ITU Recommendations; telecommunications forecasting

Forecasting international bandwidth capacity using linear and ANN methods

An artificial neural network (ANN) can improve forecasts through pattern recognition of historical data. This article evaluates the reliability of ANN methods, as opposed to simple extrapolation techniques, to forecast Internet bandwidth index data that is bursty in nature. A simple feedforward ANN model is selected as a nonlinear alternative, as it is flexible enough to model complex linear or nonlinear relationships without any prior assumptions about the data generating process. These data are virtually white noise and provides a challenge to forecasters. Using standard forecast error statistics, the ANN and the simple exponential smoothing model provide modestly better forecasts than other extrapolation methodsForecasting; international bandwidth capacity

Optimal forecasting model selection and data characteristics

Selection protocols such as Box–Jenkins, variance analysis, method switching and rules-based forecasting measure data characteristics and incorporate them in models to generate best forecasts. These protocol selection methods are judgemental in application and often select a single (aggregate) model to forecast a collection of series. An alternative is to apply individually selected models for to series. A multinomial logit (MNL) approach is developed and tested on Information and communication technology share price data. The results suggest the MNL model has the potential to predict the best forecast method based on measurable data characteristics.

Efficient Journey Planning and Congestion Prediction Through Deep Learning

The advancements of technology continuously rising over the years has seen many applications that are useful in providing users with sufficient information to make better journey plans on their own. However, commuters still find themselves going through congested routes every day to get to their destinations. This paper attempts to delineate the possibilities of improving urban mobility through big data processing and deep-learning models. Essentially, through a predictive model to predict congestion and its duration, this paper aims to develop and validate a functional journey planning mobile application that can predict traffic conditions, allowing road users to make better informed decisions to their travel plans. This paper proposes a Multi-Layered Perceptron (MLP) deep learning model for congestion prediction and supplements a Linear Regression (LR) model to predict its duration. The proposed MLP-LR model performed reasonably well with an accuracy of 63% in predicting an occurrence of congestion. Some critical discussions on further research opportunities stemming from this study is also presented

Mobile telephony and internet growth: impacts on consumer welfare

Innovation in digital technology has allowed rapid growth in mobile telephone and Internet adoption among consumers. The implication underlying the high rates of subscription growth is that consumers generally place a high valuation on telecommunication services. Moreover, since mobile telephone and Internet are predominantly telecommunication services, it is reasonable to presume that the network effect may be largely responsible for this growth. The implication of the network effect, where the consumer’s valuation of service increases with the size of the network is that subscription growth is endogenous. However, to date there have been few attempts to measure the change in consumer welfare as networks increase. Following Hausman (1981), this paper measures the change in consumer surplus based on the compensating variations approach. The result is an annual measure of the change in consumer surplus for the representative consumer for the OECD region. In addition, the approach reveals whether marginal consumer surplus is a decreasing or increasing function of network size. Measurement of the change in consumer welfare thus provides an additional tool for public policy analysis.Consumer welfare; network effect; compensating variation

Examining the relationship between college football season ticket holders\u27 service personal values and their behavioral intentions : the moderating effect of team identification.

Recently, some college football programs have experienced unsustainable attendance growth, increases in revenue discrepancies, stagnant revenue growth , and increased operating costs (Brown, 2009; Fulks, 2009; Fullerton & Morgan, 2009; Jackson, 2005; NCAA, 2009, 2010). These problems can be examined from customer service, social identification, and consumer behavior perspectives (Curtin, 1982; Katona, 1974; Wann & Branscombe, 1993; Zeithaml, 1988). This study\u27s research purposes are to understand service personal values antecedents and outcomes, and team identification\u27s moderating effect on the relationship between service personal values, and both consumption satisfaction perceptions and behavioral outcomes. A sample of college football season ticket holders at a large public university in Southeastern United States completed an online survey. Factorial multivariate analysis of variance (MANOY A), multiple regression analysis, and hierarchical regression analysis were used to analyze the data. The findings of this study indicated college football season ticket holders\u27 team identification moderated the relationship between their service value to social recognition (SYSR), and both consumption satisfaction and behavioral intentions. College football season ticket holders\u27 with low team identification level are more likely to depend on SVSR to formulate their consumption satisfaction perceptions and behavioral intentions, compared to college football season ticket holders\u27 with high team identification level. Antecedents of college football season ticket holders\u27 service personal values include number of household members, gender, university affiliation, number of years holding season tickets, and ethnicity

Dynamics of international aid in the Chinese context: a case study of the World Bank's Cixi Wetlands Project in Zhejiang Province

Environmental degradation in China, intensified by open-door reforms and industrialization, has been increasing at an alarming scale. Domestically, environmental governance has been poor, often due to institutional constraints and lack of “good practices.” However, recently there have been studies on how the “foreign factor” might have profound positive effects on capacity building in China and how international actors could lead to the successful introduction of good environmental governance. In this article, we present a study of a successful case: the World Bank Global Environmental Facility Cixi Wetlands project in Ningbo, China. The article examines the following: (a) the unique local context enabling the diffusion of international norms; (b) the factors which contribute to the World Bank's leverage role in restructuring local project governance; and (c) the changes in local environmental governance arising from the Bank's involvement. By evaluating this project, the article will demonstrate how the World Bank managed to introduce and socialize local actors into project-specific policy dialogues and procedures that enhanced local compliance with its international practices and standards

GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction

Predicting the future paths of an agent's neighbors accurately and in a timely manner is central to the autonomous applications for collision avoidance. Conventional approaches, e.g., LSTM-based models, take considerable computational costs in the prediction, especially for the long sequence prediction. To support more efficient and accurate trajectory predictions, we propose a novel CNN-based spatial-temporal graph framework GraphTCN, which models the spatial interactions as social graphs and captures the spatio-temporal interactions with a modified temporal convolutional network. In contrast to conventional models, both the spatial and temporal modeling of our model are computed within each local time window. Therefore, it can be executed in parallel for much higher efficiency, and meanwhile with accuracy comparable to best-performing approaches. Experimental results confirm that our model achieves better performance in terms of both efficiency and accuracy as compared with state-of-the-art models on various trajectory prediction benchmark datasets.Comment: 10 pages, 7 figures, 3 table

Making connections between novel transcription factors and their DNA motifs

The key components of a transcriptional regulatory network are the connections between trans-acting transcription factors and cis-acting DNA-binding sites. In spite of several decades of intense research, only a fraction of the estimated ∼300 transcription factors in Escherichia coli have been linked to some of their binding sites in the genome. In this paper, we present a computational method to connect novel transcription factors and DNA motifs in E. coli. Our method uses three types of mutually independent information, two of which are gleaned by comparative analysis of multiple genomes and the third one derived from similarities of transcription-factor-DNA-binding-site interactions. The different types of information are combined to calculate the probability of a given transcription-factor-DNA-motif pair being a true pair. Tested on a study set of transcription factors and their DNA motifs, our method has a prediction accuracy of 59% for the top predictions and 85% for the top three predictions. When applied to 99 novel transcription factors and 70 novel DNA motifs, our method predicted 64 transcription-factor-DNA-motif pairs. Supporting evidence for some of the predicted pairs is presented. Functional annotations are made for 23 novel transcription factors based on the predicted transcription-factor-DNA-motif connections