Short-term forecasting in electronics

Milojkovic, Jelena; Litovski, Vančo

doi:10.1080/00207217.2010.482025

Cited by 10 publications

(8 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our task here was to do one-step-ahead prediction. Using the already predicted value as input data for multistep-ahead prediction leads to accumulation of the prediction error what we demonstrated in [21].…”

Section: Ffap Neural Networkmentioning

confidence: 99%

Concurrent, Performance-Based Methodology for Increasing the Accuracy and Certainty of Short-Term Neural Prediction Systems

Milic

Milojkovic²,

Marković

et al. 2019

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

Accurate prediction of the short time series with highly irregular behavior is a challenging task found in many areas of modern science. Such data fluctuations are not systematic and hardly predictable. In recent years, artificial neural networks have widely been exploited for those purposes. Although it is possible to model nonlinear behavior of short time series by using ANNs, very often they are not able to handle all events equally well. Therefore, alternative approaches have to be applied. In this study, a new, concurrent, performance-based methodology that combines best ANN topologies in order to decrease the forecasting errors and increase the forecasting certainty is proposed. The proposed approach is verified on three different data sets: the Serbian Gross National Income time series, the municipal traffic flow for a particular observation point, and the daily electric load consumption time series. It is shown that the method can significantly increase the forecasting accuracy of the individual networks, regardless of their topologies, which makes the methodology more applicable. For quantitative comparison of the accuracy of the proposed methodology with that of similar methodologies, a series of additional forecasting experiments that include a state-of-the-art ARIMA modelling and a combination of ANN and linear regression forecasting have been conducted.

show abstract

Section: Ffap Neural Networkmentioning

confidence: 99%

Concurrent, Performance-Based Methodology for Increasing the Accuracy and Certainty of Short-Term Neural Prediction Systems

Milic

Milojkovic²,

Marković

et al. 2019

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

“…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 2 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

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

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

Short-term forecasting in electronics

Cited by 10 publications

References 5 publications

Concurrent, Performance-Based Methodology for Increasing the Accuracy and Certainty of Short-Term Neural Prediction Systems

Concurrent, Performance-Based Methodology for Increasing the Accuracy and Certainty of Short-Term Neural Prediction Systems

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

Contact Info

Product

Resources

About