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Posted: 07 Feb 2022 02:00

“LSTM” February 2022 — summary from Europe PMC and PLOS

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“LSTM” February 2022 — summary from Europe PMC and PLOS main image

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Europe PMC - summary generated by Brevi Assistant


This paper investigates the forecasting efficiency for credit report default swap spreads by Support Vector Machines, Group Method of Data Handling, Long Short-Term Memory and Markov changing autoregression for everyday CDS spreads of the 513 leading US firms, in the period 2009-2020. We locate that the marketplace has been less reliable during Covid-19, Nonetheless, there are no massive distinctions in prediction efficiencies prior to and throughout the Covid-19 duration.

In the last few years, with the advance of Artificial Intelligence, automatic songs make-up has been shown. First, we gather a variety of existing Guzheng songs items and transform them right into the Music Instrument Digital Interface format.

The goals this research plan is to construct and compare 2 kinds of projecting models at different time ranges for hemorrhagic fever incidence in China. It was revealed from the projecting efficiency in 2019 that ARIMA was far better than LSTM for monthly and once a week forecasting while the LSTM was better than ARIMA for everyday projecting in rolling projecting models.

The COVID-19 pandemic remains to have a significant influence on wellness and clinical infrastructure, economic situation, and farming. Deep learning models such as recurrent neural networks are well matched for designing spatiotemporal sequences. With the rapid development of Artificial Intelligence innovation, a boosting number of intelligent formulas have been used for mimicing and forecasting hydrological procedure, among which the Long Short-Term Memory network is extensively examined. The results acquired in Yalong River, Minjiang River and Jialing River reveal that raising the training information amount can efficiently decrease the over-fittings of the LSTM network; enhancing the training data amount can also boost the prediction precision and security of the LSTM network.


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PLOS - summary generated by Brevi Assistant


Objectives: This research study means to contrast and develop two sorts of forecasting models at different time scales for hemorrhagic fever occurrence in China. Results: ARIMA 12, ARIMA 52 and ARIMA were picked as the most effective fitting ARIMA model for month-to-month, day-to-day and regular incidence collection, specifically. The values of root mean square mistake, mean absolute mistake and indicate outright percent mistake of the models integrated with rolling forecasts in 2019 were less than those of the straight projecting models for both ARIMA and LSTM. It was revealed from the projecting efficiency in 2019 that ARIMA was far better than LSTM for once a week and month-to-month forecasting while the LSTM was better than ARIMA for everyday forecasting in rolling forecasting models.

The COVID-19 pandemic proceeds to have a major effect on health and medical infrastructure, economy, and farming. Deep learning models such as recurrent neural networks are well fitted for modelling spatiotemporal series. In this paper, we apply recurrent neural networks such as long short term memory, bidirectional LSTM, and encoder-decoder LSTM models for multi-step COVID-19 infection forecasting. We pick Indian states with COVID-19 hotpots and record the 2nd and first wave of infections and offer two months in advance anticipate.

Recently, online financing has developed many threats while giving lending convenience to Chinese people and medium-sized and tiny enterprises. We first utilize the BERT model to divide capitalist belief in the review info of China's on-line financing third-party information internet sites into 3 classifications and analyze the relationship between capitalist view and quantitative indications of the internet financing product purchases. The results show that the percentage of positive remarks has a positive relationship to the borrowing rate of interest rate of P2P systems that financiers agree to join for bidding process.

Second, after presenting the financier's view into the long short term memory model, the average RMSE of the 3 projection periods for obtaining rate of interest is 0. 373, and that of the borrowing period is 0. 262, which is better than the worths of various other control models.


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