A stacking neuro-fuzzy framework to forecast runoff from distributed meteorological stations

Querales, Marvin; Salas, Rodrigo; Morales, Yerel; Allende‐Cid, Héctor; Rosas, Harvey

doi:10.1016/j.asoc.2022.108535

Cited by 10 publications

(3 citation statements)

References 52 publications

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“…Future work is required in order to increase the number of subjects and thus increase the variability of the signals. Furthermore, it would be interesting to explore other methods such as neuro-fuzzy models [40,44], machine learning models [45], and deep learning techniques [46]. Moreover, analysing the wavelet domain of the signals could be relevant for healthcare applications [47,48].…”

Section: Discussionmentioning

confidence: 99%

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An Extreme Learning Machine for Blood Pressure Waveform Estimation using the Photoplethysmography Signal

Tapia¹,

Salas²,

Salinas³

et al. 2022

J. Engg. Res. & Sci.

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Blood Pressure (BP) waveform is a result of the response of the arteries to the blood ejection produced by the heart and, therefore, it is an important indicator of the state of the cardiovascular system. Currently, its measurement is performed invasively in critically ill patients who need a continuous and real time monitoring of their treatment response, however, it is possible to measure the BP, continuously and non-invasively, in non-critical patients to detect, monitor and control possible hypertensive events. Nevertheless, current non-invasive techniques can cause discomfort in patients and they are not used in critically ill patients. Consequently, non-Invasive and minimally-Intrusive methodologies (nImI) are required to estimate BP and its waveform. In the current study, the performance of machine learning algorithms, specifically the Extreme Learning Machine (ELM) algorithm, is evaluated to estimate both Blood Pressure and its waveform from the Photoplethysmography (PPG) signal and its first derivative's (VPG) waveforms. A total of 15 healthy volunteers participated in this study. They performed two handgrips, which is isometric maneuver to induce controlled BP rises. The first handgrip is used to train ELM and the second handgrip is used to test the ELM. Our results show that there are high correlation performances (0.98) between the estimated and measured BP waveforms, and a relative error of 3.3 ± 1.4%. An arterial volume-clamp at the middle finger is used as the gold-standard measurement. Meanwhile, BP extreme values estimations, Systolic BP (SBP) and Diastolic BP (DBP), are also performed. ELMs have a performance with an average RMSE of 5.9 ± 2.7 mmHG for SBP and 4.8 ± 2.0 mmHg for DBP and, an average relative error of 5.0 ± 2.7% for SBP and 7.0 ± 4.0% for DBP.

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Section: Discussionmentioning

confidence: 99%

“…They correspond to reBP signal extreme values. To build the ELM models, we have used an ensemble approach to combine the inputs of different signals [39,40].…”

Section: Approach 2: Sbp and Dbp Estimatesmentioning

confidence: 99%

An Extreme Learning Machine for Blood Pressure Waveform Estimation using the Photoplethysmography Signal

Tapia¹,

Salas²,

Salinas³

et al. 2022

J. Engg. Res. & Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Time series analysis and forecasting are essential in many areas of application, such as finance and marketing [1], air pollution [2], electricity consumption [3], and weather forecasting [4,5], among others. However, selecting the appropriate model strongly depends on the degree of predictability of the time series [6].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Long-Term Dependencies in Time Series Using Recurrent Artificial Neural Networks

Ubal,

Di-Giorgi,

Contreras-Reyes

et al. 2023

MAKE

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Long-term dependence is an essential feature for the predictability of time series. Estimating the parameter that describes long memory is essential to describing the behavior of time series models. However, most long memory estimation methods assume that this parameter has a constant value throughout the time series, and do not consider that the parameter may change over time. In this work, we propose an automated methodology that combines the estimation methodologies of the fractional differentiation parameter (and/or Hurst parameter) with its application to Recurrent Neural Networks (RNNs) in order for said networks to learn and predict long memory dependencies from information obtained in nonlinear time series. The proposal combines three methods that allow for better approximation in the prediction of the values of the parameters for each one of the windows obtained, using Recurrent Neural Networks as an adaptive method to learn and predict the dependencies of long memory in Time Series. For the RNNs, we have evaluated four different architectures: the Simple RNN, LSTM, the BiLSTM, and the GRU. These models are built from blocks with gates controlling the cell state and memory. We have evaluated the proposed approach using both synthetic and real-world data sets. We have simulated ARFIMA models for the synthetic data to generate several time series by varying the fractional differentiation parameter. We have evaluated the proposed approach using synthetic and real datasets using Whittle’s estimates of the Hurst parameter classically obtained in each window. We have simulated ARFIMA models in such a way that the synthetic data generate several time series by varying the fractional differentiation parameter. The real-world IPSA stock option index and Tree Ringtime series datasets were evaluated. All of the results show that the proposed approach can predict the Hurst exponent with good performance by selecting the optimal window size and overlap change.

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Multistep prediction for earthworks unloading duration: a fuzzy Att-Seq2Seq network with optimal partitioning and multi-time granularity modeling

Zhang,

Wang,

et al. 2023

Neural Comput & Applic

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A stacking neuro-fuzzy framework to forecast runoff from distributed meteorological stations

Cited by 10 publications

References 52 publications

An Extreme Learning Machine for Blood Pressure Waveform Estimation using the Photoplethysmography Signal

An Extreme Learning Machine for Blood Pressure Waveform Estimation using the Photoplethysmography Signal

Predicting the Long-Term Dependencies in Time Series Using Recurrent Artificial Neural Networks

Multistep prediction for earthworks unloading duration: a fuzzy Att-Seq2Seq network with optimal partitioning and multi-time granularity modeling

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