Internet traffic prediction with deep neural networks

Jiang, Weiwei

doi:10.1002/itl2.314

Cited by 33 publications

(26 citation statements)

References 18 publications

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“…In this section, I will present different models used to predict the number of confirmed cases and deaths of COVID-19. These models were chosen because they have been shown to be effective in a range of problems in different domains [ 11 – 13 ]. I wanted to validate their performance in the new crown outbreak prediction task.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets

Wang

2022

BioMed Research International

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The COVID-19 pandemic has been a global crisis affecting billions of people and causing countless economic losses. Different approaches have been proposed for combating this crisis, including both medical measures and technical innovations, e.g., artificial intelligence technologies to diagnose and predict COVID-19 cases. While there is much attention being paid to the USA and China, little research attention has been drawn to less developed countries, e.g., India. In this study, I conduct an analysis of the COVID-19 epidemic in India, with datasets collected from different sources. Several machine learning models have been built to predict the COVID-19 spread, with different combinations of input features, in which the Transformer is proven as the most precise one. I also find that the Facebook mobility dataset is the most useful for predicting the number of confirmed cases. However, I find that the datasets from different sources are not very effective when predicting the number of deaths caused by the COVID-19 infection.

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Section: Methodsmentioning

confidence: 99%

COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets

Wang

2022

BioMed Research International

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“…Three deep learning models are explored in this study, namely, LSTM, GRU, TCN. These models have been proven effective in other prediction problems [16][17][18].…”

Section: Modelsmentioning

confidence: 99%

Sentiment-based Bitcoin Movement Prediction with Deep Learning

Li¹

2022

Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2

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Bitcoin movement prediction has been a research topic, especially with the Bitcoin bubble period during the COVID-10 pandemic era. It has been discussed that sentiment factors are influential in the prediction process. In this study, to validate the helpfulness of these emotion-related features, three open datasets with sentiment are used, and three types of neural networks, including LSTM, GRU, and TCN, are chosen as the prediction models, using a regression model to study the relationship between them. The result shows that the sentiment-based factors did help in two of the three datasets.

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“…Two advanced deep learning models are evaluated in this study, namely, LSTM and GRU, both of which are improved variants of RNNs. These models have been successful for many similar problems [13][14]. As for comparison, the normal feed-forward artificial neural network (ANN) is also used in this study.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Traffic prediction with deep learning for vehicular communication network

Wang

Liu

2022

International Conference on Smart Transportation and City Engineering (STCE 2022)

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Nowadays, the construction of Internet of vehicles provides a lot of convenience for urban public safety and transportation. Among them, the prediction of traffic flow based on traffic road condition prediction is very important. Due to the interference of many external factors, such as unexpected events and weather, this problem is difficult. In recent years, due to its strong learning ability, deep learning has made many breakthroughs in various image classification and data analysis and processing problems. Among them, the traffic flow prediction method based on deep learning models can provide a set of rich tools, which can make good use of these data to build the Internet of vehicles. In this paper, we compare the deep learning model with the traditional model, and further attempt the preprocessing methods such as Wavelet and Singular Spectrum Analysis (SSA) to improve the performance of the deep learning model. Based on a real International Mobile Equipment Identity (IMEI) data set, we evaluated LSTM, GRU, ARIMA, SVR and ANN models. Considering the problem of whether the deep learning method is suitable for real-time traffic prediction, we counted and compared the training preprocessing time. We found that compared with LSTM and GRU, ARIMA is still competitive and Wavelet or SSA is not helpful as we expected.

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Internet traffic prediction with deep neural networks

Cited by 33 publications

References 18 publications

COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets

COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets

Sentiment-based Bitcoin Movement Prediction with Deep Learning

Traffic prediction with deep learning for vehicular communication network

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