A Study on the Prediction of Book Borrowing Based on ARIMA-SVR Model

Pan, Yan; Jin, Xiuling; Li, Yuliang; Da-lian, Chen; Zhou, Juan

doi:10.1016/j.procs.2021.05.057

Cited by 9 publications

(2 citation statements)

References 8 publications

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“…In recent years, the machine learning method has been widely used in traffic flow prediction because it can highly adapt to high-noise data, reduce errors by cyclic iteration, and deeply excavate the inherent laws of data. The prediction methods of machine learning mainly include the artificial neural network (ANN) [16], support vector regression (SVR) [17], and so on. However, ANN has a serious overfitting problem, and lacks generalization ability, therefore it is easy to fall into a local optimal problem, and its prediction effect is unsatisfactory.…”

Section: Related Workmentioning

confidence: 99%

Traffic Volume Prediction: A Fusion Deep Learning Model Considering Spatial–Temporal Correlation

Zheng

Dong

et al. 2021

Sustainability

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In this paper, a fusion deep learning model considering spatial–temporal correlation is proposed to solve the problem of urban road traffic flow prediction. Firstly, this paper holds that the traffic flow of a section in the urban road network not only depends on the fluctuation of its own time series, but is also related to the traffic flow of other sections in the whole region. Therefore, a traffic flow similarity measurement method based on wavelet decomposition and dynamic time warping is proposed to screen the sections which are similar to the traffic flow state of the target section. Secondly, in order to improve the prediction accuracy, the unstable time series are reconstructed into stationary time series by differential method. Finally, taking the extracted traffic flow data of a similar section as an independent variable and the traffic flow data of target section as dependent variable, we input the above variables into the proposed CNN-LSTM fusion deep learning model for traffic flow prediction. The results show that the proposed model has a higher accuracy and stability than the other benchmark models. The MAPE can reach 92.68%, 93.39%, 85.14%, and 76.14% at a time interval of 5 min, 15 min, 30 min, and 60 min, and the other evaluation indexes are also better than the rest of the benchmark models.

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Section: Related Workmentioning

confidence: 99%

Traffic Volume Prediction: A Fusion Deep Learning Model Considering Spatial–Temporal Correlation

Zheng

Dong

et al. 2021

Sustainability

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“…The results indicate that the combined algorithm (LSTM-SVR) can utilize a small amount of data to realize the multivariate non-Gaussian conditional simulation with spatial interpolation prediction more accurately. Pan et al [33] presented a hybrid prediction model based on ARIMA and SVR, and the experimental results showed that the mixed model had high prediction accuracy and could accurately describe the complex change trend of the time series of the number of borrowers.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR

Liu

2023

Applied Sciences

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Currently, accidents in civil engineering buildings occur frequently, resulting in significant economic damage and a large number of casualties. Therefore, it is particularly important to predict the trend of early damage to building structures. Early structural damages are difficult to correctly identify, and obtaining the required accuracy using a single traditional time-series prediction method is difficult. In this study, we propose a novel method based on the integration of support vector regression (SVR) and long short-term memory (LSTM) networks to predict structural damage trends. First, the acceleration vibration signal of the structure is decomposed using the variational mode decomposition (VMD) method, and the decomposed components are transformed with Hilbert transform to obtain the instantaneous frequency. Then, the instantaneous frequency is input into the LSTM–SVR integrated model for damage trend prediction. The results indicate that the VMD method effectively eliminates modal aliasing and decomposes various intrinsic components of the signal. Compared with individual LSTM and SVR models, the integration model has a higher prediction accuracy for small samples in a chaotic time series that is 6.56%, 2.56%, and 3.7%, respectively. The standard deviation of the absolute percentage error (SDAPE) values of the three operating conditions under the integrated method decreased 0.0994, 0.0869, and 0.0921, which improved the stability of prediction. The mean absolute percentage error (MAPE) of the integration method is an order of magnitude higher than that of the LSTM model.

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Prediction of annual rice imports emphasizes on systematic error reduction with smoothing series and optimal parameter selection techniques

Sujjaviriyasup

2024

Neural Comput & Applic

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A Study on the Prediction of Book Borrowing Based on ARIMA-SVR Model

Cited by 9 publications

References 8 publications

Traffic Volume Prediction: A Fusion Deep Learning Model Considering Spatial–Temporal Correlation

Traffic Volume Prediction: A Fusion Deep Learning Model Considering Spatial–Temporal Correlation

Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR

Prediction of annual rice imports emphasizes on systematic error reduction with smoothing series and optimal parameter selection techniques

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