Technology forecasting: A case study of computational technologies

Adamuthe, Amol C.; Thampi, G. T.

doi:10.1016/j.techfore.2019.03.002

Cited by 63 publications

(35 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the literature, there are several other five-parameter growth models, such as the model of Bass [31] for market diffusion, which has also been used to model the diffusion of electric cars [32]. We decided to use the BP model as it is known to be flexible [33] and as it includes as special cases several simple three-parameter models which have been used previously in the analysis and forecasting of technology diffusion and business trends [34,35]. Specifically, the modeling of the automotive market has often used logistic growth [5,23] or the Gompertz model [36][37][38].…”

Section: Other Growth Modelsmentioning

confidence: 99%

The Markets of Green Cars of Three Countries: Analysis Using Lotka–Volterra and Bertalanffy–Pütter Models

Ziegler

Brunner

Kühleitner

2020

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

Did the diesel scandal of 2015 affect the market for cars? We consider this question in relation to Germany, Austria, and Switzerland. Starting with historical registration data of cars with different drivetrain technologies, we considered each technology in isolation and fitted a five-parameter Bertalanffy–Pütter (BP) growth model to the stocks of cars. We used this model as it generalizes several well-known three-parameter models, which are distinguished by their exponent pair, e.g., Brody model BP (0, 1), West model BP (0.75, 1), and logistic growth BP (1, 2). We then used these models to derive a Lotka–Volterra (LV) model for the co-evolution of the (annual) market shares of the different drivetrain technologies. We augmented this model by a consideration of model uncertainty and found that initially all technologies were in a state of competition, except for Austria, which changed in 2015 to a predator–prey situation with diesel as the sole prey. This analysis of model uncertainty compared the best-fitting growth curve with the growth trajectories of other likely (Akaike weight 5% or higher) models of BP type. We conclude with remarks about open innovation.

show abstract

Section: Other Growth Modelsmentioning

confidence: 99%

The Markets of Green Cars of Three Countries: Analysis Using Lotka–Volterra and Bertalanffy–Pütter Models

Ziegler

Brunner

Kühleitner

2020

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

show abstract

“…While various models for scientific growth have been proposed, there is as of yet no consensus on which specific model should be used. In previous work researchers chose a number of known trends, searched for their frequency in one or more databases, then fitted their candidate models to the resulting curves (Bettencourt et al, 2006;Trappey & Wu, 2008;Adamuthe & Thampi, 2019). However, the weakness in these approaches lies in the small number of trends used, and in the fact that they are manually chosen by the researchers, which introduces the prospect of selection bias.…”

Section: Introductionmentioning

confidence: 99%

“…Perhaps the most similar work to ours is Adamuthe and Thampi (2019). They fit the Logistic and Gompertz models to six time series gathered via keyword searches for mainframes, minicomputers, cluster computing, grid computing, autonomic computing, and cloud computing on four datasets (two patent datasets, IEEE, and Science Direct).…”

Section: Introductionmentioning

confidence: 99%

Modelling trend life cycles in scientific research using the Logistic and Gompertz equations

2021

View full text Add to dashboard Cite

Scientific topics vary in popularity over time. In this paper, we model the life cycles of 200 trending topics by fitting the Logistic and Gompertz models to their frequency over time in published abstracts. Unlike other work, the topics we use are algorithmically extracted from large datasets of abstracts covering computer science, particle physics, cancer research, and mental health. We find that the Gompertz model produces lower median error, leading us to conclude that it is the more appropriate model. Since the Gompertz model is asymmetric, with a steep rise followed a long tail, this implies that scientific topics follow a similar trajectory. We also explore the case of double-peaking curves and find that in some cases, topics will peak multiple times as interest resurges. Finally, when looking at the different scientific disciplines, we find that the lifespan of topics is longer in some disciplines (e.g. cancer research and mental health) than it is others, which may indicate differences in research process and culture between these disciplines.

show abstract

“…Decision making is one of the most crucial tasks in many domains and often decisions are based on the most accurate forecast available in the respective domains. A large number of areas, such as energy [1,2], economics [3], infrastructure [4,5], health [6,7], agriculture [8,9], defense [10], education [11,12], technology [13,14], geo-science [15], climate [16] and structural engineering [17] among several others, are looking forward to benefits that can be achieved with time series forecasting. Time series are consecutive sequences of values ordered with respect to time.…”

Section: Introductionmentioning

confidence: 99%

ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling

Bokde

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Andersen

2020

Energies

View full text Add to dashboard Cite

This paper introduces an R package ForecastTB that can be used to compare the accuracy of different forecasting methods as related to the characteristics of a time series dataset. The ForecastTB is a plug-and-play structured module, and several forecasting methods can be included with simple instructions. The proposed test-bench is not limited to the default forecasting and error metric functions, and users are able to append, remove, or choose the desired methods as per requirements. Besides, several plotting functions and statistical performance metrics are provided to visualize the comparative performance and accuracy of different forecasting methods. Furthermore, this paper presents real application examples with natural time series datasets (i.e., wind speed and solar radiation) to exhibit the features of the ForecastTB package to evaluate forecasting comparison analysis as affected by the characteristics of a dataset. Modeling results indicated the applicability and robustness of the proposed R package ForecastTB for time series forecasting.

show abstract

Technology forecasting: A case study of computational technologies

Cited by 63 publications

References 18 publications

The Markets of Green Cars of Three Countries: Analysis Using Lotka–Volterra and Bertalanffy–Pütter Models

The Markets of Green Cars of Three Countries: Analysis Using Lotka–Volterra and Bertalanffy–Pütter Models

Modelling trend life cycles in scientific research using the Logistic and Gompertz equations

ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling

Contact Info

Product

Resources

About