Time series prediction based on intuitionistic fuzzy cognitive map

Luo, Chao; Zhang, Nannan; Wang, Xingyuan

doi:10.1007/s00500-019-04321-8

Cited by 20 publications

(8 citation statements)

References 39 publications

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“…For each cluster, it chooses the feature that best represents the set containing less redundant and more dispersed information. This allows us to maintain a good representation of the original data set with a complexity similar to PCA [22].…”

Section: Processing Datamentioning

confidence: 99%

“…In addition, the ability to predict when these events are going to happen would allow the performing of corrective actions to improve the QoE, e.g., proactively lowering the video resolution or allocating more resources to specific microservices. Further, new prediction models should be studied and their outcomes evaluated to take full advantage of the deep learning potential [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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On the Identification and Prediction of Stalling Events to Improve QoE in Video Streaming

Martinez‐Caro

Cano

2021

Electronics

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Monitoring the Quality of user Experience is a challenge for video streaming services. Models for Quality of User Experience (QoE) evaluation such as the ITU-T Rec. P.1203 are very promising. Among the input data that they require are the occurrence and duration of stalling events. A stalling even5 is an interruption in the playback of multimedia content, and its negative impact on QoE is immense. Given the idiosyncrasy of this type of event, to count it and its duration is a complex task to be automated, i.e., without the participation of the user who visualizes the events or without direct access to the final device. In this work, we propose two methods to overcome these limitations in video streaming using the DASH framework. The first method is intended to detect stalling events. For simplicity, it is based on the behavior of the transport layer data and is able to classify an IP packet as belonging (or not) to a stalling event. The second method aims to predict if the next IP packet of a multimedia stream will belong to a stalling event (or not), using a recurrent neural network with a variant of the Long Short–Term Memory (LSTM). Our results show that the detection model is able to spot the occurrence of a stalling event before being experienced by the user, and the prediction model is able to forecast if the next packet will belong to a stalling event with an error rate of 10.83%, achieving an F1 score of 0.923.

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Section: Processing Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Identification and Prediction of Stalling Events to Improve QoE in Video Streaming

Martinez‐Caro

Cano

2021

Electronics

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“…Among conventional ML methods, random forests have shown a higher accuracy to predict disease activity compared to support vector machines (SVM) or logistic regression in non-rheumatic disorders such as heart failure or diabetes [11]. Fuzzy cognitive maps is another increasingly used ML-method for clinical prediction tasks and decision support [12]. Using data from electronic medical records (EMR), ML has successfully predicted RA flares in a small number of RA patients by a random forest as a classical ML method [13,14].…”

Section: Introductionmentioning

confidence: 99%

Personalized prediction of disease activity in patients with rheumatoid arthritis using an adaptive deep neural network

et al. 2021

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Background Deep neural networks learn from former experiences on a large scale and can be used to predict future disease activity as potential clinical decision support. AdaptiveNet is a novel adaptive recurrent neural network optimized to deal with heterogeneous and missing clinical data. Objective We investigate AdaptiveNet for the prediction of individual disease activity in patients from a rheumatoid arthritis (RA) registry. Methods Demographic and disease characteristics from over 9500 patients and 65.000 visits from the Swiss Quality Management (SCQM) database were used to train and evaluate the network. Patient characteristics, clinical and patient reported outcomes, laboratory values and medication were used as input features. DAS28-BSR served as a target to predict active RA and future numeric individual disease activity by classification and regression. Results AdaptiveNet predicted active disease defined as DAS28-BSR >2.6 at the next visit with an overall accuracy of 75.6% (SD +- 0.7%) and a sensitivity and specificity of 84.2% (SD +- 1.6%) and 61.5% (SD +- 3.6%), respectively. Prediction performance was significantly higher in patients with a disease duration >3 years and positive rheumatoid factor. Regression allowed forecasting individual DAS28-BSR values with a mean squared error (MSE) of 0.9 (SD +- 0.05). This corresponds to a 8% deviation between estimated and real DAS28-BSR values. Compared to linear regression, random forest and support vector machines, AdaptiveNet showed an increased performance of over 7% in MSE. Medication played a minor role in the prediction of RA disease activity. Conclusion AdaptiveNet has a superior capacity to predict numeric RA disease activity compared to classical machine learning architectures. All investigated models had limitations in low specificity.

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“…ere have been many published works proposing different algorithms to conduct the predictive analysis of economic chaotic time series, but they seem to have their downsides. Although the cognitive map method can use the optimized model to predict the future value when there are new data pairs, it fails to enable the number of blocks and units in each block unit to be expanded according to a certain growing pattern [5]. e online learning method can be linked to the correlation criterion, but this method may contain strong jitter in the overall change trend and has a certain degree of randomness [6].…”

Section: Introductionmentioning

confidence: 99%

Predictive Analysis of Economic Chaotic Time Series Based on Chaotic Genetics Combined with Fuzzy Decision Algorithm

Tan

2021

Complexity

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The irreversibility in time, the multicausality on lines, and the uncertainty of feedbacks make economic systems and the predictions of economic chaotic time series possess the characteristics of high dimensionalities, multiconstraints, and complex nonlinearities. Based on genetic algorithm and fuzzy rules, the chaotic genetics combined with fuzzy decision-making can use simple, fast, and flexible means to complete the goals of automation and intelligence that are difficult to traditional predicting algorithms. Moreover, the new combined method’s ergodicity can perform nonrepetitive searches in a global scope, hence improving the algorithm’s accuracy and efficiency. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of the prediction of economic chaotic time series, elaborated the development background, current status, and future challenges of the combined algorithm of chaotic genetics with fuzzy decision, introduced the basic principles of chaotic genetic algorithm and fuzzy decision algorithm, constructed a prediction model for economic chaotic time series, performed parameter synchronization optimization and moderate function construction, analyzed the prediction processes of economic chaotic time series, conducted phase space reconstruction and correlation dimension calculation, and finally carried out a simulation experiment with its result analysis. The study results show that the algorithm of chaotic genetics combined with fuzzy decision-making can dynamically adjust chaotic mutation operators and summarize fussy expert experiences. The phase space of its reconstructed chaotic attractor has high-precision predictability and can find orderly processes from changeable economic results, which in turn can be used to analyze and predict the complex economic chaotic time series. The study results of this paper provide a reference for further research on predictive analysis of economic chaotic time series based on chaotic genetics combined with fuzzy decision algorithm.

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Time series prediction based on intuitionistic fuzzy cognitive map

Cited by 20 publications

References 39 publications

On the Identification and Prediction of Stalling Events to Improve QoE in Video Streaming

On the Identification and Prediction of Stalling Events to Improve QoE in Video Streaming

Personalized prediction of disease activity in patients with rheumatoid arthritis using an adaptive deep neural network

Predictive Analysis of Economic Chaotic Time Series Based on Chaotic Genetics Combined with Fuzzy Decision Algorithm

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