Forecasting of Multivariate Time Series via Complex Fuzzy Logic

Yazdanbakhsh, Omolbanin; Dick, Scott

doi:10.1109/tsmc.2016.2630668

Cited by 28 publications

(9 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other purpose may be to determine potential cause-effect relationships that exist between the different variables involved, for which it is common to use dependence, interdependence or structural approaches [43]. Nevertheless, it is also feasible to deal with this type of problem by jointly employing inferential models of a heterogeneous nature [44][45][46][47][48], both statistical and symbolic, with the common objective of representing the same reality. In this case, the diagnostic process that allows discerning between a patient who suffers from OSA and one who does not.…”

Section: Discussionmentioning

confidence: 99%

Design and Development of an Intelligent Clinical Decision Support System Applied towards the Diagnosis of Suspected Obstructive Sleep Apnea Patients from the Patient’s Health Profile and Symptomatology

Casal-Guisande¹,

Ceide-Sandoval²,

Mosteiro-Añón³

et al. 2023

Preprint

View full text Add to dashboard Cite

Obstructive Sleep Apnea (OSA) is nowadays one of the respiratory pathologies with a higher in-cidence globally in developed countries. This situation led to an increase in the demand for medical appointments and diagnostic studies related to that condition, especially those based on poly-somnographies and cardiorespiratory polygraphies. These studies are limited in resources, causing long waiting lists with the subsequent impact on the patients’ health. Furthermore, it is necessary to mention that OSA’s symptomatology is not very specific, and it is typically present in the general population (excessive sleepiness, snore, etc.). In this regard, this paper proposes a novel intelligent clinical decision support system for the diagnosis of OSA which could be used to help medical teams, both in primary care settings and in units specialized in respiratory pathologies. The aim of the proposed system is to help discriminate the patients suspected of suffering from the pathology from those who are not. To this end, two types of information sets of heterogeneous nature are consid-ered. The first one encompasses objective data, related to the patient's health profile with infor-mation usually available in electronic health records. The second type comprises subjective data, referred to the symptomatology reported by the patient in a previous interview. To process the first group of information, a Machine Learning classification algorithm is used, Bagged Trees in this case. For processing the second information set, related with the symptomatology of the patient, a col-lection of expert systems based on fuzzy inferential systems arranged in cascade are employed. As a result, the system is able to determine two risk indicators related to the patient's risk of suffering from OSA: the Statistical Risk and the Symbolic Risk respectively. Subsequently, by interpreting both risk indicators mentioned it will be possible to determine the severity of the patients’ health, proposing a preliminary evaluation on their condition. For the initial tests of the system, a software artifact has been built using a dataset with 4,978 selected patients, suspected of suffering from OSA, from the Álvaro Cunqueiro Hospital in Vigo. The results obtained are promising, demonstrating the potential usefulness of this type of tools in medical diagnosis. Once the system has been validated with new data from clinical environments, it is considered as possible to obtain a relevant improvement in the quality of the healthcare services, and a reduction in the associated costs.

show abstract

Section: Discussionmentioning

confidence: 99%

Design and Development of an Intelligent Clinical Decision Support System Applied towards the Diagnosis of Suspected Obstructive Sleep Apnea Patients from the Patient’s Health Profile and Symptomatology

Casal-Guisande¹,

Ceide-Sandoval²,

Mosteiro-Añón³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Using the experimental results on five real datasets, the values of MSE and NDEI from applying ANCFIS are less than those of the compared models. ANCFIS was improved as FANCFIS to deal with multivariate time series problem [27,41]. This model was designed to maintain the accuracy and decrease time computing of ANCFIS.…”

Section: Fuzzy Inference System In Complex Fuzzy Setmentioning

confidence: 99%

M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing

et al. 2020

View full text Add to dashboard Cite

Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS.

show abstract

“…In modeling, the mathematical model needs to rely on empirical knowledge to select parameters and set disturbances, which leads to difficulty in realizing the modeling process and large prediction errors. Regression analysis, grey theory, fuzzy theory [7], and time series are statistical prediction methods. A hybrid autoregressive (AR)-EMD-SVR model was developed by Duan W et al [8].…”

Section: Introductionmentioning

confidence: 99%

Ship Attitude Prediction Model Based on Cross-Parallel Algorithm Optimized Neural Network

et al. 2022

View full text Add to dashboard Cite

In recent years, with the development of modern technology and industrialization, the shipbuilding industry has become an essential field in a country. Offshore operations, such as helicopter takeoff and landing, and ship-to-ship cargo replenishment, need an accurate prediction of ship motion attitude, not only to improve efficiency but also to protect crew members' lives. However, using the single model has limited influence on prediction. Therefore, this paper proposed a Cross-Parallel optimization algorithm framework, which made full use of the development ability of the Harris Hawk optimization algorithm (HHO) and the searching ability of the sine-cosine algorithm (SCA). In this paper, we use Rosenbrock and Rastrigin functions for testing. The results show that the cross-parallel optimization algorithm has faster search speed and stronger optimization capability. The optimization algorithm is used to optimize the hyperparameters of long short-term memory (LSTM) neural networks. It improves the network's training ability to predict the ship's attitude. The proposed method in this paper is compared with 9 models such as BP, LSTM, GRU, BILSTM and TCN. It is tested with three degrees of freedom under different sea states. MAE and RMSE are used as the evaluation indexes of this paper. In the six sets of experimental results, compared with the other nine models, the method in this paper can reduce the MAE by up to 9.2%, 7.2%, 4.7%, 5.4%, 6.4%, and 6.8%, respectively. When using RMSE as the evaluation index, the decrease was at most 10.1%, 8.7%, 5.6%, 7.1%, 7.5%, and 8.2%. The experimental results show that the method in this paper has the highest prediction accuracy.

show abstract

Forecasting of Multivariate Time Series via Complex Fuzzy Logic

Cited by 28 publications

References 77 publications

Design and Development of an Intelligent Clinical Decision Support System Applied towards the Diagnosis of Suspected Obstructive Sleep Apnea Patients from the Patient’s Health Profile and Symptomatology

Design and Development of an Intelligent Clinical Decision Support System Applied towards the Diagnosis of Suspected Obstructive Sleep Apnea Patients from the Patient’s Health Profile and Symptomatology

M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing

Ship Attitude Prediction Model Based on Cross-Parallel Algorithm Optimized Neural Network

Contact Info

Product

Resources

About