On prediction in electronics

Milojkovic, Jelena; Bojanić, Slobodan; Litovski, Vančo

doi:10.1109/miel.2010.5490484

Cited by 4 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These were reported in [1,2,3,4] and may be characterized as follows: use of simple ANNs (only several neurons in the hidden layer, between one and ten), and use of short time series. There are two reasons for the later.…”

Section: Ann Application For Forecastingmentioning

confidence: 99%

“…Second, as it is frequently clamed in the forecasting literature, only the most recent data are of prime importance in the behavior of many natural and social phenomena. In the next, we will briefly describe our ANN solution as given in [1,2,3,4].…”

Section: Ann Application For Forecastingmentioning

confidence: 99%

“…t=9 and f(t)=18. 4, was compared with the predictions obtained in order to validate the new methods.…”

Section: Ann Application For Forecastingmentioning

confidence: 99%

“…After publishing these results we implemented the new methods to prediction in two new areas: electricity load [2] and microelectronic developments [3,4]. Among other, in [1,2,3,4] the claim "One major criticism about the NN model is that it demands a great deal of training data and relatively long training period for robust generalization" [5] was denied. Namely, neural networks with small numbers of hidden neurons were implemented and prediction were produced based on extremely reduced sets of data within very short training time periods.…”

Section: Introductionmentioning

confidence: 99%

“…While comparisons were given in [1,2,3,4] with some other forecasting methods no discussion of the Grey theory [6,7] and, accordingly, no adequate comparisons, were presented.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

ANN versus Grey theory based forecasting methods implemented on short time series

Milojkovic

Litovski

2010

10th Symposium on Neural Network Applications in Electrical Engineering

Self Cite

View full text Add to dashboard Cite

Two modern concepts implemented for forecasting based on reduced time series are contrasted. Results obtained by use of artificial neural nets (ANNs), already discussed at this conference, are compared with the ones obtained by implementation of the so called Grey theory or Grey Model (GM). Particularly, feed-forward accommodated for prediction (FFAP) and time controlled recurrent (TCR) ANNs are used along with the GM(1,1) algorithm for one-and two-steps-ahead forecasting of various quantities (obsolete computers, electricity loads, number of fixed telephones etc). Advantages of the ANN concept are observed. The GM(1,1) was studied in the appendix and compared with no advantages against the least-meansquares approximation by an exponential.

show abstract

Section: Ann Application For Forecastingmentioning

confidence: 99%

Section: Ann Application For Forecastingmentioning

confidence: 99%

“…t=9 and f(t)=18. 4, was compared with the predictions obtained in order to validate the new methods.…”

Section: Ann Application For Forecastingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…While comparisons were given in [1,2,3,4] with some other forecasting methods no discussion of the Grey theory [6,7] and, accordingly, no adequate comparisons, were presented.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

ANN versus Grey theory based forecasting methods implemented on short time series

Milojkovic

Litovski

2010

10th Symposium on Neural Network Applications in Electrical Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

Forecasting Based on Short Time Series Using ANNs and Grey Theory – Some Basic Comparisons

Milojkovic

Litovski

Nieto–Taladriz

et al. 2011

Advances in Computational Intelligence

View full text Add to dashboard Cite

Comments on the Model Testing by Goodwin and Meeran (2012): Comparison of the Utility‐based and the Gompertz Curve Approach for High‐technology Product Sales Forecasting

Gnibba-Yukawa¹,

Decker²

2012

J of Product Innov Manag

View full text Add to dashboard Cite

Starting from a comprehensive examination of recent empirical studies focusing on consumer behavior in high‐technology markets and the resulting identification of factors probably affecting individual buying decisions as well as aggregate product sales, Decker and Gnibba‐Yukawa developed and empirically verified a utility‐based sales forecasting approach in their earlier work. Based on data for 14 consumer electronic products and using the Gompertz curve as a benchmark, Goodwin and Meeran carried out a “more extensive testing” of this proposal. However, at least from a practical point of view, the plausibility of their testing framework regarding the market potential m is not unquestionable. This paper, therefore, first discusses some theoretical aspects of both approaches by addressing issues challenged by Goodwin and Meeran, especially regarding the use of short time series and the consideration of replacement purchases. Then, the quasi‐endogenous estimation method for m favored by Goodwin and Meeran for the Gompertz curve is examined in terms of sensitivity to better understand its influence on sales forecasts, and the adequacy of the suggested range for m in the case of the approach by Decker and Gnibba‐Yukawa is investigated. In addition, the results presented in Goodwin and Meeran are considered from a more distant perspective, and possible causes of the variations in forecasting accuracy are discussed, which finally reveals that the forecasting performance of the utility‐based approach is not that “disappointing” as claimed. It provides more accurate (or at least equivalent) forecasts than the Gompertz curve approach in 64% of the cases considered. Furthermore, if product 14 (portable MP3 players) is excluded from the analysis because of the nonconsideration of probably existing product improvement effects, then the utility‐based approach, on average, outperforms the benchmark in all forecasting years. Altogether, this suggests that the approach by Decker and Gnibba‐Yukawa could achieve more accurate forecasts when applying a more reasonable range for m, rather than varying it between 2 and 15 times the cumulative sales by the end of year 7 as proposed by Goodwin and Meeran. It turns out that the Gompertz curve approach can perform on a par with the utility‐based approach in high‐technology product sales forecasting based on short time series if the market potential m is estimated exogenously. A combination of the outcomes of both approaches can even lead to more accurate forecasts as when being used individually insofar as composite forecasting seems to be a practicable approach to the problem of shorter time series compelled by the accelerated diffusion speed in high‐technology markets, rather than relying on one presumably “best” model.

show abstract

On prediction in electronics

Cited by 4 publications

References 7 publications

ANN versus Grey theory based forecasting methods implemented on short time series

ANN versus Grey theory based forecasting methods implemented on short time series

Forecasting Based on Short Time Series Using ANNs and Grey Theory – Some Basic Comparisons

Comments on the Model Testing by Goodwin and Meeran (2012): Comparison of the Utility‐based and the Gompertz Curve Approach for High‐technology Product Sales Forecasting

Contact Info

Product

Resources

About