Identification of Lags in Nonlinear Autoregressive Time Series Using a Flexible Fuzzy Model

Veloz, Alejandro; Salas, Rodrigo; Allende‐Cid, Héctor; Allende, Héctor; Moraga, Claudio

doi:10.1007/s11063-015-9438-1

Cited by 13 publications

(9 citation statements)

References 48 publications

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“…All the time series data are obtained from the Time Series Data Library (TSDL) (Hyndman 2013), which is an open repository of time series datasets. These four time series data are also used in Wu and Lee (2015) and Veloz et al (2016). The four time series data are: (a) the Laser dataset, which is from the fluctuations of a far-infrared laser and measured in a physics laboratory experiment; (b) Sunspots, which contain the annual amount of observed sunspots from 1700 to 1987; (c) ESTSP2007 dataset, which is from the European Symposium on Time Series Prediction competition 2007; (d) Poland electricity load, which presents the electricity load values of Poland from 1990s.…”

Section: Resultsmentioning

confidence: 99%

“…Evaluating the accuracy and reliability of the prediction results is an important part of forecasting analysis. In this paper, four error indices are considered: root-mean-squared error (RMSE), mean absolute error (MAE), normalized root-mean-square error (NRMSE) (Wu and Lee 2015), and normalized mean square error (NMSE) (Veloz et al 2016). These error indices are shown as follows:…”

Section: Resultsmentioning

confidence: 99%

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A time series forecasting based on cloud model similarity measurement

et al. 2018

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In this paper, a local cloud model similarity measurement (CMSM) is proposed as a novel method to measure the similarity of time series. Time series similarity measurement is an indispensable part for improving the efficiency and accuracy of prediction. The randomness and uncertainty of series data are critical problems in the processing of similarity measurement. CMSM obtains the internal information of time series from the general perspective and local trend using the cloud model, which reduces the uncertainty of measurement. The neighbor set is selected from time series by CMSM and used to construct a prediction model based on least squares support vector machine. The proposed technique reduces the potential for overfitting and uncertainty and improves model prediction quality and generalization. Experiments were performed with four datasets selected from Time Series Data Library. The experimental results show the feasibility and effectiveness of the proposed method.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A time series forecasting based on cloud model similarity measurement

et al. 2018

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“…The recursive method, also known as an iterated method, can lead to poor accuracy in long forecasting horizons. According to Pouzols and Barros [64] , and Veloz et al [65] , the recursive strategy uses forecasting values as a model’s input to forecasting the next predicted values. Its main disadvantage is to accumulate the previous forecasting errors in the recursive process.…”

Section: Methodsmentioning

confidence: 99%

Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods

Ribeiro

Mariani

Coelho

2020

Journal of Biomedical Informatics

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Epidemiological time series forecasting plays an important role in health public systems, due to its ability to allow managers to develop strategic planning to avoid possible epidemics. In this paper, a hybrid learning framework is developed to forecast multi-step-ahead (one, two, and three-month-ahead) meningitis cases in four states of Brazil. First, the proposed approach applies an ensemble empirical mode decomposition (EEMD) to decompose the data into intrinsic mode functions and residual components. Then, each component is used as the input of five different forecasting models, and, from there, forecasted results are obtained. Finally, all combinations of models and components are developed, and for each case, the forecasted results are weighted integrated (WI) to formulate a heterogeneous ensemble forecaster for the monthly meningitis cases. In the final stage, a multi-objective optimization (MOO) using the Non-Dominated Sorting Genetic Algorithm – version II is employed to find a set of candidates’ weights, and then the Technique for Order of Preference by similarity to Ideal Solution (TOPSIS) is applied to choose the adequate set of weights. Next, the most adequate model is the one with the best generalization capacity out-of-sample in terms of performance criteria including mean absolute error (MAE), relative root mean squared error (RRMSE), and symmetric mean absolute percentage error (sMAPE). By using MOO, the intention is to enhance the performance of the forecasting models by improving simultaneously their accuracy and stability measures. To access the model’s performance, comparisons based on metrics are conducted with: (i) EEMD, heterogeneous ensemble integrated by direct strategy, or simple sum; (ii) EEMD, homogeneous ensemble of components WI; (iii) models without signal decomposition. At this stage, MAE, RRMSE, and sMAPE criteria as well as Diebold–Mariano statistical test are adopted. In all twelve scenarios, the proposed framework was able to perform more accurate and stable forecasts, which showed, on 89.17% of the cases, that the errors of the proposed approach are statistically lower than other approaches. These results showed that combining EEMD, heterogeneous ensemble and WI with weights obtained by optimization can develop precise and stable forecasts. The modeling developed in this paper is promising and can be used by managers to support decision making.

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“…Likewise, the variables with the lowest linear association were the Pacific Decadal Oscillation index (PDO), El Niño multivariate Southern Oscillation Index (MEI), Southern Oscillation index (SOI), and N34 (highly associated with N12). It should be noted that there is a wide range of methods to identify significant variables and associated lags, such as those shown in [26]; however in the document, the methodology suggested by [24] and specified in the previous section was used (Step iii, Section 2.3). Table 3 presents a summary of the main models obtained, with their indicators of the goodness of fit and forecast.…”

Section: Variable Standardizationmentioning

confidence: 99%

Polynomial and Wavelet-Type Transfer Function Models to Improve Fisheries’ Landing Forecasting with Exogenous Variables

Vivas

Allende‐Cid

Salas

et al. 2019

Entropy

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It is well known that environmental fluctuations and fishing efforts modify fishing patterns in various parts of the world. One of the most affected areas is northern Chile. The reduction of the gaps in the implementation of national fisheries’ management policies and the basic knowledge that supports the making of such decisions are crucial. That is why in this research, a transfer function method with variable coefficients is proposed to forecast monthly disembarkation of anchovies and sardines in northern Chile, taking into account the incidence of large-scale climatic variables on landings. The method uses a least squares procedure and wavelets to expand the coefficients of the transfer function. Linear estimators of the time varying coefficients are proposed, followed by a truncation of the wavelet expansion up to an appropriate scale. Finally, the estimators for the transfer function coefficients are obtained by using the inverse wavelet transformation. Research results suggest that the transfer function models with variable coefficients fit the behavior of the anchovies’ landing with great accuracy, while the use of transfer function models with constant coefficients fits sardines’ landings better. Both fisheries’ landings could be explained to a large extent from the large scale climatic variables.

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Identification of Lags in Nonlinear Autoregressive Time Series Using a Flexible Fuzzy Model

Cited by 13 publications

References 48 publications

A time series forecasting based on cloud model similarity measurement

A time series forecasting based on cloud model similarity measurement

Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods

Polynomial and Wavelet-Type Transfer Function Models to Improve Fisheries’ Landing Forecasting with Exogenous Variables

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