Technological growth curves

Young, Peg

doi:10.1016/0040-1625(93)90042-6

Cited by 98 publications

(12 citation statements)

References 59 publications

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“…Very similar results from US production data were later obtained by Wiorkowski [34] 12 and Cleveland & Kaufmann [27]. 13 This highlights a generic weakness of curve-fitting techniques, namely: different functional forms often fit the data comparably well but give very different estimates of the URR [65].…”

Section: Overview Application and Evaluation Of Curve-fitting Techniques (A) Production Over Time Techniquessupporting

confidence: 66%

“…12 Wiorkowski [34] compared a 'generalized Richards' model (which can take an exponential, logistic, or Gompertz form depending upon the parameters chosen) with a cumulative Weibull and found that they fitted US cumulative production data equally well but led to significantly different URR estimates (445 Gb and 235 Gb, respectively). 13 Cleveland & Kaufmann [27] fitted a logistic curve to US production data through to 1988 and found that the adjusted R further cycles are expected in the future. This highlights the second generic weakness of curvefitting techniques, namely their inability to anticipate future cycles of discovery and production in aggregate regions.…”

Section: Overview Application and Evaluation Of Curve-fitting Technimentioning

confidence: 99%

“…Similarly, a growth curve may be fitted to time-series data on cumulative oil discoveries in a region and used to estimate the URR and forecast the future rate of oil discovery. Although dismissed by critics as 'trendology' [7,8], these curve-fitting techniques have much in common with more sophisticated methods of estimating recoverable resources and forecasting future supply [9,10], as well as with the use of growth curves to forecast technological diffusion [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using growth curves to forecast regional resource recovery: approaches, analytics and consistency tests

Sorrell

Speirs

2014

Phil. Trans. R. Soc. A.

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There is growing concern about the depletion of hydrocarbon resources and the risk of near-term peaks in production. These concerns hinge upon contested estimates of the recoverable resources of different regions and the associated forecasts of regional production. Beginning with Hubbert, an influential group of analysts have used growth curves both to estimate recoverable resources and to forecast future production. Despite widespread use, these ‘curve-fitting’ techniques remain a focus of misunderstanding and dispute. The aim of this paper is to classify and explain these techniques and to identify both their relative suitability in different circumstances and the expected level of confidence in their results. The paper develops a mathematical framework that maps curve-fitting techniques onto the available data for conventional oil and highlights the critical importance of the so-called ‘reserve growth’. It then summarizes the historical origins, contemporary application and strengths and weaknesses of each group of curve-fitting techniques and uses illustrative data from a number of oil-producing regions to explore the extent to which these techniques provide consistent estimates of recoverable resources. The paper argues that the applicability of curve-fitting techniques is more limited than adherents claim, the confidence bounds on the results are wider than commonly assumed and the techniques have a tendency to underestimate recoverable resources.

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Section: Overview Application and Evaluation Of Curve-fitting Techniques (A) Production Over Time Techniquessupporting

confidence: 66%

Section: Overview Application and Evaluation Of Curve-fitting Technimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using growth curves to forecast regional resource recovery: approaches, analytics and consistency tests

Sorrell

Speirs

2014

Phil. Trans. R. Soc. A.

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“…Depending on the particular application, the requirements for a “good” model are different. Evidence from the forecasting literature has demonstrated the lack of correlation between retrospective fit and forecasting accuracy (e.g., Rao, 1985; Young, 1993).…”

Section: Review Of the Diffusion‐modeling And The Utility‐based Approachmentioning

confidence: 99%

Sales Forecasting in High-Technology Markets: A Utility-Based Approach

Decker

Gnibba-Yukawa

2010

Journal of Product Innovation Management

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Accurate sales projections are of vital importance to the profitability and long-term survival of high-tech companies. This is especially true in the growth stage of product innovation, because major investments and marketing decisions are made in this phase. By examining recent empirical studies focusing on consumer behavior in high-technology markets, several factors are identified that can affect individual buying decisions and aggregate sales, namely, interpersonal communication, democratization of innovation, direct and indirect network effects, forward-looking behavior, and consumer heterogeneity. Against this background the diffusion-modeling and the utility-based approach are reviewed in terms of their basic conception and their applicability to the markets concerned. Based on this investigation a sales forecasting model for high-tech products, specifically in the growth stage, is developed. The model has a utility-theoretic background and a logistic structure. Since data are scarce in this early stage of the product life cycle two versions of the new approach are discussed, an extended version considering forward-looking behavior and a more parsimonious (''myopic'') one. The performance of the new model is demonstrated using real sales data on the CD player, the DVD player/recorder, and the digital camera market. The empirical comparisons include alternative specifications of the Bass diffusion model as well as a proportional hazard model and consist of two steps. First, the models are checked as to whether they are able to represent the sales data at all. It is shown that both versions of the proposed model are at least equivalent, if not superior, to the models used as benchmarks in terms of fit. In the next step, the models are applied to predict future sales in the three markets. The resulting forecasts show that the proposed model performs significantly better than its benchmarks. Its parsimony enables reliable predictions, even in cases, where only short time series are available for parameter estimation. The model is able to anticipate decreasing diffusion rates as they occur at the end of the growth stage and, thus, helps to avoid overoptimistic sales forecasts, which may cause severe economic damages. The new approach is easy to calibrate and can be applied without specialized econometric expertise.

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“…Prediction algorithms are useful in a wide range of scientific disciplines, such as earth sciences [7, 9], finance [8, 11], computer science [12, 13], and engineering [14–16]. Several methods have been proposed for time-series forecasting [2] and competitions regarding accuracy have been conducted, utilizing statistical [11, 17] and machine learning procedures [2, 10, 15]; however, the prediction horizon regards only a small percentage (~20-30%) of the given data extension. Similarly, for less uncertain problems as the extrapolation of curves defined by polynomials (splines) [18–20], extrapolation can cause unpredictable results, and their extension should be short [21].…”

Section: Introductionmentioning

confidence: 99%

Numerical Solution for the Extrapolation Problem of Analytic Functions

Bakas

2019

Research

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In this work, a numerical solution for the extrapolation problem of a discrete set of n values of an unknown analytic function is developed. The proposed method is based on a novel numerical scheme for the rapid calculation of higher order derivatives, exhibiting high accuracy, with error magnitude of O(10−100) or less. A variety of integrated radial basis functions are utilized for the solution, as well as variable precision arithmetic for the calculations. Multiple alterations in the function’s direction, with no curvature or periodicity information specified, are efficiently foreseen. Interestingly, the proposed procedure can be extended in multiple dimensions. The attained extrapolation spans are greater than two times the given domain length. The significance of the approximation errors is comprehensively analyzed and reported, for 5832 test cases.

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Technological growth curves

Cited by 98 publications

References 59 publications

Using growth curves to forecast regional resource recovery: approaches, analytics and consistency tests

Using growth curves to forecast regional resource recovery: approaches, analytics and consistency tests

Sales Forecasting in High-Technology Markets: A Utility-Based Approach

Numerical Solution for the Extrapolation Problem of Analytic Functions

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