Interpolating time series based on fuzzy cluster analysis problem

Authors

  • L. D. Nghiep College of Basis Science, Nam Can Tho University, Can Tho city, Vietnam.
  • V. V. Tai College of Natural Science, Can Tho University, Can Tho city, Vietnam.
Abstract:

This study proposes the model for interpolating time series to use them  to forecast effectively for future. This model is established based on the improved fuzzy clustering analysis problem, which is implemented by the Matlab procedure. The proposed model is illustrated by a data set and tested for many other datasets, especially for 3003 series in  M3-Competition data. Comparing  to the existing models, the proposed model always gives the best result. We also apply  the proposed model in forecasting  the salt peak for a coastal province of Vietnam. Examples and applications show the potential of the studied problem.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

Time Series Seasonal Analysis Based on Fuzzy Transforms

We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of an assigned output. In the first example, we use the daily weather dataset of the municipality of Naples (Italy) starti...

full text

Time Series Seasonal Analysis Based on Fuzzy Transforms 2 3

1 Università degli Studi di Napoli Federico II, Dipartimento di Architettura, via Toledo 402, 80134 Napoli 5 (Italy); fdimarti,[email protected] 6 * Correspondence: [email protected]; Tel.: +39-081-253-8907; Fax: +39-081-253-8905 7 8 Abstract: We define a new seasonal forecasting method based on fuzzy transforms. We use the best 9 interpolating polynomial for extracting the trend of the time serie...

full text

On Interpolating Power Series

We derive a simple error estimate for equally spaced, polynomial interpolation of power series that does not require the uniform bounds on derivatives of the Cauchy remainder. The key steps are expressing Newton coefficients in terms of Stirling numbers S(i, j) of the second kind and applying the concavity of lnS(i, j).

full text

Cluster analysis based on fuzzy relations

In this paper, cluster analysis based on fuzzy relations is investigated. Tamura’s max-min n-step procedure is extended to all types of max-t compositions. A max-t similarity-relation matrix is obtained by beginning with a proximity-relation matrix based on the proposed max-t n-step procedure. Then a clustering algorithm is created for the max-t similarityrelation matrix. Three critical max-t c...

full text

Residual analysis using Fourier series transform in Fuzzy time series model

In this paper, we propose a new residual analysis method using Fourier series transform into fuzzy time series model for improving the forecasting performance. This hybrid model takes advantage of the high predictable power of fuzzy time series model and Fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...

full text

A NEW APPROACH BASED ON OPTIMIZATION OF RATIO FOR SEASONAL FUZZY TIME SERIES

In recent years, many studies have been done on forecasting fuzzy time series. First-order fuzzy time series forecasting methods with first-order lagged variables and high-order fuzzy time series forecasting methods with consecutive lagged variables constitute the considerable part of these studies. However, these methods are not effective in forecasting fuzzy time series which contain seasonal...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 17  issue 3

pages  151- 161

publication date 2020-06-01

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023