Forecasting technology diffusion with the Richards model

The Richards model has a shape parameter m that allows it to fit any sigmoidal curve. This article demonstrates the ability of a modified Richards model to fit a variety of technology diffusion curvilinear data that would otherwise be fit by Bass, Gompertz, Logistic, and other models. The performance of the Richards model in forecasting was examined by analyzing fragments of data computed from the model itself, where the fragments simulated either an entire diffusion curve but with sparse data points, or only the initial trajectory of a diffusion curve but with dense data points. It was determined that accurate parameter estimates could be obtained when the data was sparse but traced out the curve at least up to the third inflection point (concave down), and when the data was dense and traced out the curve up to the first inflection point (concave up). Rogers' Innovation I, II and III are discussed in the context of the Richards model. Since m is scale independent, the model allows for a typology of diffusion curves and may provide an alternative to Rogers' typology.     

Forecasting system thermal efficiencies in technology importing countries

Forecasting the thermal efficiency of a set of power plants belonging to a country is important for energy, water, and environmental planning.In this article an analysis of the way in which the system thermal efficiency (STE) changes is provided, and a forecasting model for the electric utilities of technology importing countries is developed.The model is based on the identification of the speed with which technology is transferred from abroad and of the adjustment process of the system's efficiency to that of the best plant's.The application of the calculation to the South African utility shows that the STE in the year 2015 will be 20 to 25 percent higher than that in the year 1983.     

Forecasting VoWLAN technology for the Taiwan mobile telecommunication industry

Disruptive innovation is always a great challenge to the management of incumbent firms, especially in fast-changing industries. In this study, scenarios were developed to facilitate strategic decision-making by incumbent mobile telecommunications firms that confront the threats of disruptive technology of voice over wireless local area network (VoWLAN). Combining various possible outcomes of uncertain conditions and strategic alternatives available to the incumbent firms, six scenarios were developed: incremental evolution, disruptive evolution, cost deterrence, fierce competition, market pre-emption and market convergence. The results show that a passive ‘do-nothing’ strategy by incumbent firms leads to failure if the disruptive technology is inevitable. However, firms can slow the pace of disruptive technology by applying a price-cut strategy and enjoy several years of profits in the process. Industrial insights and strategic implications obtained from all the scenarios are discussed.     

Forecasting emerging technologies with the aid of science and technology databases

Short term forecasting was applied to 20 emerging technologies under the “Machine and Materials” category based on the Vision 2023 foresight study previously conducted for Turkey. This scientometric approach uses the most suitable keywords linked to the technology in question and determines the number of publications and patents in those fields for a given year. Database analysis of publications and patents in the previous 11 years indicates that while the majority of the top 20 technologies identified by the experts are indeed emerging (i.e. the number of research and/or patenting in these technologies is increasing), some of them have not actually attracted too much interest in the science and technology (S&T) community. Forecasts based on S-curves indicate steady growth in some of the selected technologies. There is a high correlation between the number of scientific publications and patents in most of the technologies investigated. The method is proposed as a simple and efficient tool to link national foresight efforts to international S&T activities and to obtain quantitative information for prioritized technologies that could be used for technology management and decision making for research funding and technology investment.     

Forecasting technology diffusion: a new generalisation of the logistic model

The present research proposes a new generalisation of the logistic model aiming at technology diffusion forecasting. Regarding criticisms and failures reported in the literature to apply logistic function for long-term forecasting, in our work we focused on short-term accuracy of forecast. To formulate the model, based on mathematical approximation, at first the differential equation governing the diffusion process is found and then by solving the derived differential equation, the forecast function is obtained. In all steps, mathematical tools from numerical analysis are used. We compared the New Generalized Logistic Model with eight of the most renowned models in the literature. The model led to more accurate fits and forecasts than those obtained from other models we applied for comparison.     

Research Forecasting for Health Information Technology (HIT), using technology intelligence

Due to the rapid pace of change in technology and its impact on society, there is an increasing demand for use of Technology Forecasting methods to improve policy planning and implementation. One such area is the field of Health Care and the impact of Health Information Technology (HIT) on this field. Using HIT has shown to be associated with reduced cost, improved quality, and better patient experience; yet HIT adoption has been slow. Therefore, there is a need to better understand the HIT adoption processes in order to meet the evolving requirements for health care delivery.We propose collecting Technology Intelligence for use in Research Forecasting as part of the larger HIT Technology Forecasting efforts. In this study, we systematically probed for HIT-related technology intelligence in the fields of Information Systems, Engineering Management, and Medical Informatics. Results of our analysis show that all three fields are active in Health IT research, but could benefit from further collaboration. We were also able to identify instances of emerging journals and emerging topics in Health IT research. We conclude that it is indeed plausible and meaningful to collect technology intelligence on HIT adoption, to support the overall goal of improving healthcare delivery.     

Forecasting technology costs via the experience curve — Myth or magic?

To further understand the effectiveness of experience curves to forecast technology costs, a statistical analysis using historical data is carried out. Three hypotheses are tested using available datasets that together shed light on the historical ability of experience curves to forecast technology costs. The results indicate that the Single Factor Experience Curve is a useful forecasting model when errors are viewed in their log format. Practitioners should note that due to the convexity of the log curve a mean overestimation of potential cost reductions can arise as values are converted into monetary units. Time is also tested as an explanatory variable, however forecasts made with endogenous learning based on cumulative capacity as used in traditional experience curves are shown to be vastly superior. Furthermore the effectiveness of increasing weights for more recent data is tested using Weighted Least Squares with exponentially increasing weights. This results in forecasts that are less biased, though have increased spread when compared to Ordinary Least Squares.     

Forecasting the electric transformation in transportation: the role of battery technology performance

In an effort to encourage consumers to purchase electric vehicles (EVs), the government has been funding battery research to solve some of these problems. This paper presents a study using technology forecasting using data envelopment analysis (TFDEA) to forecast future battery performance characteristics. The results were compared against the performance goals established by the US Department of Energy (DOE). We find that the foreseen progress of EV battery performance will be insufficient to meet the DOE projected goals for the range that EVs can travel before running out of power. Therefore, a new battery technology must be developed because the incremental improvements in current battery technologies leave EVs considerably short of the DOE performance specification for longer trip ranges.     

基于最优组合预测模型的港口集装箱吞吐量预测

根据港口集装箱吞吐量非线性增长等特点,建立了三次指数平滑预测模型、灰色系统预测模型及BP神经网络预测模型等单项预测模型。鉴于单项预测模型的局限性,提出了以测试数据的预测误差绝对值加权和最小为最优化准则的最优组合预测模型,采用线性规划的方法确定最优组合的权系数。最后,给出一个实例进行应用和分析。     

Forecasting broadband provision

The provision of broadband telecommunication services in the United States is spatially heterogeneous. Previous studies find that urban locales typically have a larger selection of access platforms and providers when compared to rural or remote areas. Therefore, the ability to forecast broadband provision is important to understanding regional trends in telecommunications competition and availability. This paper provides a comparative analysis of cross-sectional and spatial econometric forecasts of broadband provision, with results suggesting that the integration of spatially referenced demand-side variables significantly improve the accuracy of short and mid-range forecasts.     

On Forecasting Discontinuities

Too much has been written about forecasting trends via “envelope curves.” To plot a straight line on log-paper is a no-brainer. The special phenomenology of phase changes, catastrophes, or “crashes,” and their key evolutionary role is the subject of this article. There is no unique way of forecasting such events, but one indicator is an apparent inconsistency between two or more extrapolations with each other. Alternatively, a catastrophe may be signalled when a trend extrapolation encounters a natural limit. A number of possible discontinuity scenarios are sketched, although overall, the article laments the lack of theorization in forecasting disconuities.     

Forecasting first births

This study is concerned with one aspect of the family cycle, namely, the transition from young married to young married with small children. The focus is on developing models to forecast entries into this latter stage for the purpose of marketing research. "Using ordinary least squares, forecasting models were estimated for (1) total number of first births, (2) number of white first births, and (3) number of nonwhite first births." Models are estimated for both the United States and California using data from official sources.     

Forecasting and Futurology

The Institute of Applied Economic and Social Research has published forecasts of the Australian economy since the late 1960s. These forecasts (usually 12 to 18 months ahead) have been dominated by short-term macroeconomic factors. Compared with when the IAESR commenced its forecasting, there are now many forecasters who concentrate on the performance of the Australian economy over the short term. There is, however, a dearth of regular commentary which focuses on policy options for the medium to long term. This article is the first of many which will aim to fill this perceived gap. In so doing we shall adopt a wider concept of forecasting called futurology. In this approach, many constraints of traditional forecasting are relaxed. In particular, we allow responses by economic agents to policy changes. This will suggest plausible situations in which different outcomes are possible. Our purpose is not to suggest what the future will be, but what it might be under different scenarios. Here, we put together some necessary ingredients to facilitate futurology. We review recent Australian economic performance, consider the implications of meeting a goal of five per cent unemployment by the year 2000 and outline some important influences on likely future growth. We suggest that new growth theory may provide a policy framework to achieve both high growth and low unemployment, noting the constraints imposed by increasing globalisation. In subsequent articles, we shall employ these ingredients to write out plausible timepaths setting out how desirable end-points (including the reduction of unemployment) may be achieved.     

A New Economy Forecasting Method Based on Data Barycentre Forecasting Method

A new and useful method of technology economics, parameter estimation method, was presented in light of the stability of gravity center of object in this paper. This method could deal with the fitting and forecasting of economy volume and could greatly decrease the errors of the fitting and forecasting results. Moreover, the strict hypothetical conditions in least squares method were not necessary in the method presented in this paper, which overcame the shortcomings of least squares method and expanded the application of data barycentre method. Application to the steel consumption volume forecasting was presented in this paper. It was shown that the result of fitting and forecasting was satisfactory. From the comparison between data barycentre forecasting method and least squares method, we could conclude that the fitting and forecasting results using data barycentre method were more stable than those of using least squares regression forecasting method, and the computation of data barycentre forecasting method was simpler than that of least squares method. As a result, the data barycentre method was convenient to use in technical economy.     

Forecasting Australian marriage rates

"The paper discusses the problem of modelling demographic variables for the purpose of forecasting. Two empirical model selection procedures are applied to suggest final form forecasting equations for Australian marriage rates. The suggested models are then assessed by comparing their post-sample forecast performance with that of univariate ARMA-type models of marriage rates which are regarded as approximations to marriage rate final equation models. In this instance the ARMA models are preferred for forecasting purposes. The properties of the ARMA model forecasts are then examined and the modelling strategy is contrasted with the regression method used by Withers."     

灰色预测在产品价格预测中的应用

人类社会进入21世纪后，企业间面临更加激烈的市场竞争，价格则是这场竞争中决定企业竞争成败及目标能否实现的重要因素，企业要在竞争中立于不败之地，必须及时掌握市场产品价格动态，并对市场价格的变化趋势做出科学的预测，本借助灰色预测理论，阐述了市场产品价格预测的方法。     

Forecasting Canadian mortgage rates

Mortgage rates are one of the important drivers of the housing market. While there is a literature looking at the pass-through effect from Central Bank rates to mortgage rates, there is less known about how useful Central Bank rates are for forecasting mortgage rates. This article uses a selection of models (ARIMA, ARIMAX, BATS, state space error, trend seasonal (ETS), Holt Winter, random walk, simple exponential smoothing (SES), OLS and VAR) to forecast Canadian 5-year conventional mortgage rates. Based on RMSE, regression-based approaches like ARIMAX or OLS that use Central Bank rates to forecast mortgage rates are preferred when it comes to forecasting Canadian mortgage rates 6 or 12 months into the future, respectively.     

Forecasting and random processes

On the simple model of a (univariate) random process some general problems of “random logic” are discussed. New random approaches to explorative, normative and systematic (normex) forecasting are developed, and some applications are suggested.     

Forecasting inflation in China

This paper forecasts inflation in China over a 12-month horizon. The analysis runs 15 alternative models and finds that only those considering many predictors via a principal component display a better relative forecasting performance than the univariate benchmark.