Neural networks of combination of forecasts for data with long memory pattern

Masood A. Badri

    Research output: Contribution to conferencePaperpeer-review

    3 Citations (Scopus)

    Abstract

    This paper examines three neural network models for forecasting to better understand the performance of neural networks for the case when the data exhibit a long memory pattern. The first model is simple one consisting of the time series values. The second model uses the same inputs as the first model with one additional input representing the combination (average) of five univariate time series forecasting models. The third model consists of all these individual time series forecasts in which the neural network is allowed to combine them on its own way. To obtain the optimum networks, the effect of network characteristics such as the training parameters, the number of hidden layers, and the testing and training percentages are simulated.

    Original languageEnglish
    Pages359-364
    Number of pages6
    Publication statusPublished - 1996
    EventProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA
    Duration: Jun 3 1996Jun 6 1996

    Other

    OtherProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)
    CityWashington, DC, USA
    Period6/3/966/6/96

    ASJC Scopus subject areas

    • Software

    Fingerprint

    Dive into the research topics of 'Neural networks of combination of forecasts for data with long memory pattern'. Together they form a unique fingerprint.

    Cite this