Prediction of time series using an analysis filter bank of LSTM units

Mej­ía, José; Avelar-Sosa, Liliana; Mederos, Boris; Ramírez, Everardo Santiago; Román, José David Díaz

doi:10.1016/j.cie.2021.107371

Cited by 23 publications

(8 citation statements)

References 21 publications

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“…This last classifier GNB is one with the lowest computational costs, so a future work could be to adapt the distribution of the classifier to that of the data from network packets. Finally, in this work, it was decided to use classifiers that are faster to train and easier to integrate with the classifier module, unlike other works, such as [3] , where more complex architectures such as LSTM are proposed. It was also decided not to calculate features through complex transformations such as the principal component analysis, which is proposed in [11] .…”

Section: Resultsmentioning

confidence: 99%

“…SDNs have increased recently because of their flexible management and monitoring capabilities, which has been possible because of their centralized architecture, which can manage a set of switches by modifying flow table entries on demand for better adaptation [2] . The control plane in SDN uses special protocols, such as OpenFlow, which enables the network managers to monitor statistics, decide strategies, and interact with the switches, thus enabling an improvement of the network performance [3] .…”

Section: Introductionmentioning

confidence: 99%

“…In [3] , the authors propose an SDN that uses in its control plane section a traffic classifier that detects the traffic of the data plane and helps the QoS module to modify flow rules for the switches. This classifier is based on deep learning.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Smart architecture for software-defined networking

Mejía

Cruz‐Mejía

Acosta-Favela

et al. 2022

CULCyT

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Software-defined networks (SDN) seek to solve the problems in current network schemes by simplifying their management through their reprogrammability and accessibility to the overall network infrastructure. One aspect to improve in SDN-based schemes is the precise classification of your traffic load, this can improve various aspects such as quality of service, dynamic access control, prioritized random access, among others. This research aims to propose a conceptual architecture of SDN and evaluate different machine learning methods for traffic classification. To this end, SDN architectures are analyzed and different modules are proposed to strengthen their management with the help of low computational cost classifiers. The architecture proposes the following main modules: Capture network traces module, Learning Engine module, and ML-model and Flow classifier. To determine the model to be used in the Learning Engine and Flow classifier modules, different classifiers were evaluated using a database of network traffic, as a result, it was determined that the gradient boosting algorithm is the most suitable to be integrated with the proposed SDN architecture.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Smart architecture for software-defined networking

Mejía

Cruz‐Mejía

Acosta-Favela

et al. 2022

CULCyT

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“…The LSTM model's MAE, RMSE, and MAPE are reduced as a result. LSTM-based and noiselayered convolutional networks were presented [45]. Noise cannot be accurately managed, however, RMSE can be improved for various mobile window lengths.…”

Section: Literature Reviewmentioning

confidence: 99%

Scheduling and Predictive Maintenance for Smart Toilet

2023

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Modern society needs bathrooms. Poor sanitation is caused by worn-out appliances and expensive cleaning. The technique also requires an inexpensive, dependable sensor. This study had three goals. Creating an IoT administration platform is the main goal. Literature evaluations assess the merits and downsides of existing systems. Second, we suggest predictive maintenance to assist predict bathroom equipment breakdowns. Finally, a scheduling algorithm was used to determine how many janitors to hire. We'll measure the model's effectiveness and make future recommendations. Infrared, temperature and humidity sensors create an IoT bathroom. Sensors have been studied to understand how to adapt them to the hygienic and private toilet environment. Sensor accuracy and cost-effectiveness could be enhanced with more development and testing. The Auto-Regressive Integrated Moving Average (ARIMA) model accurately predicts time series lags, making it a good candidate for predictive maintenance. Long Short-Term Memory (LSTM) is good in time series predictions, therefore it's fair to compare the two. We use the ARIMA model to handle Remaining Useful Life (RUL) prediction techniques by altering Moving Average (MA) and Auto-Regressive (AR). A genetic algorithm is used to create a janitorial cleaning schedule. The genetic algorithm was proposed to schedule cleaning workers. This approach improves the genetic algorithm by studying soft and hard scheduling restrictions. The Greedy algorithm is used to compare. Experimental evaluations reveal that the suggested model ARIGA meets both goals.

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“…LSTM is used to forecast the volatility of wind power because of its unique memory cell and nonlinear gating unit to regulate the information flow in and out of the cell [20,21], which can mine the potential temporal correlation in wind power volatility. There are also many studies on improving LSTM [22].…”

Section: Introductionmentioning

confidence: 99%

Wind power forecast based on broad learning system and simplified long short term memory network

Han

Wang

et al. 2022

IET Renewable Power Gen

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Due to its strong randomness and volatility, the modes of wind power are complex. After decomposing wind power time series into three subseries, the complex modes are deconstructed and each subseries maintain unique characteristics. Aiming at the characteristics of each subseries, a wind power forecast method based on Broad Learning System (BLS) and Simplified Long Short Term Memory (SLSTM) is proposed. Firstly, the decomposed subseries is analysed. The first layer subseries reflects the main amplitude changes caused by ramp events and the second represents the local fluctuations accompanying ramp events. Due to the randomness of ramp events, new modes appear frequently in the two subseries. The third layer subseries consists of continuous irregular jitter. Then, the BLS is introduced to forecast the first and second layer subseries. By adjusting its structure dynamically, various modes of ramp events can be learned from amplitude change and accompanying fluctuation. Additionally, according to the characteristics of the third layer subseries, continuous jitter needn't be transmitted in long‐term memory. Setting the forget gate to 0, SLSTM is proposed to forecast the third layer subseries. Finally, the wind power data from the Belgian ELIA website is used to verify the superiority of the proposed method.

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Prediction of time series using an analysis filter bank of LSTM units

Cited by 23 publications

References 21 publications

Smart architecture for software-defined networking

Smart architecture for software-defined networking

Scheduling and Predictive Maintenance for Smart Toilet

Wind power forecast based on broad learning system and simplified long short term memory network

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