Integration of Fuzzy OWL Ontologies and Fuzzy Time Series in the Determination of Faulty Technical Units

Yarushkina, Nadezhda; Andreev, Ilya; Moshkin, Vadim; Moshkina, Irina

doi:10.1007/978-3-030-24289-3_40

Cited by 7 publications

(1 citation statement)

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“…To check the presence of a trend, the time series is divided into 2 parts. This criterion is based on the statement: if the time series has a tendency, the averages calculated for each set separately should differ significantly among themselves [ 15 ]. Null hypothesis on the absence of a tendency reduces to testing the hypothesis of the equality of the means of two normally distributed populations.…”

Section: Time Series Metrics For Solving the Prediction Problemmentioning

confidence: 99%

An Approach to the Selection of a Time Series Analysis Method Using a Neural Network

Yashin

Moshkina

Moshkin

2020

Lecture Notes in Computer Science

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In this paper, a technique for selecting individual methods in a combined time series forecasting model is described. A neural network at the input of which a vector of time series metrics is proposed. The metrics corresponds to significant characteristics of the time series. The values of the metrics are easily computed. The neural network calculates the estimated prediction error for each model from the base set of the combined model. The proposed selection method is most effective for short time series and when the base set contains a lot of complex prediction methods. The developed system was tested on the time series from the CIF 2015-2016 competitions. According to the result of the experiment, the application of the developed system allowed to reduce the average forecast error from 13 to 9 percent.

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Section: Time Series Metrics For Solving the Prediction Problemmentioning

confidence: 99%