A hybrid ARIMA-LSTM model optimized by BP in the forecast of outpatient visits

Deng, Yamin; Fan, Huifang; Wu, Shiman

doi:10.1007/s12652-020-02602-x

Cited by 27 publications

(20 citation statements)

References 31 publications

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“…[102] proposed a hybrid method combining ARIMA time series and support vector machine to forecast corn futures prices.And it is verified that the method has outstanding advantages in the forecasting of corn futures prices. A hybrid model based on the combination of autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) was proposed by Yamin Deng et al [103]. It predicts the number of outpatient visits in the hospital, which proves that the hybrid model has higher prediction accuracy and more stable model performance than the single LSTM and ARIMA model.…”

Section: )Hybrid Model Based On Arima and Machine Learningmentioning

confidence: 99%

Forecast Methods for Time Series Data: A Survey

et al. 2021

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Research on forecasting methods of time series data has become one of the hot spots. More and more time series data are produced in various fields. It provides data for the research of time series analysis method, and promotes the development of time series research. Due to the generation of highly complex and large-scale time series data, the construction of forecasting models for time series data brings greater challenges. The main challenges of time series modeling are high complexity of time series data, low accuracy and poor generalization ability of prediction model. This paper attempts to cover the existing modeling methods for time series data and classify them. In addition, we make comparisons between different methods and list some potential directions for time series forecasting.

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Section: )Hybrid Model Based On Arima and Machine Learningmentioning

confidence: 99%

Forecast Methods for Time Series Data: A Survey

et al. 2021

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“…Lee et al adopted a two-dimensional hierarchical decision tree scheme to forecast weekly influenza outpatient visits [ 7 ]. Deng et al used backpropagation neural networks to optimize the model hybridized with ARIMA and LSTM model to calculate outpatient visits [ 8 ]. Prashanthi et al created a SARIMA model to estimate the ophthalmology outpatient visits using historical data from the electronic medical record system [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Method for Improving the Prediction of Outpatient Visits for Hospital Management: Bayesian Autoregressive Analysis

Wei

Tan

et al. 2022

Computational and Mathematical Methods in Medicine

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The number of outpatient visits is generally influenced by various factors that are difficult to quantify and obtain, resulting in some irregular fluctuations. The traditional statistical methodology seldom considers these uncertainties. Accordingly, this paper presents a Bayesian autoregressive (AR) analysis to propose a forecasting framework to cope with the strict requirements. The AR model was conducted to identify the linear and autocorrelation relationships of historical series, and Bayesian inference was used to correct and optimize the AR model parameters. Posterior distribution of parameters was stably and reliably obtained by Gibbs sampling on the condition of the convergent Markov chain. Meanwhile, the lag orders of the AR model were adjusted based on the series characteristics. To increase the variability and generality of the dataset, the developed Bayesian AR model was evaluated at seven hospitals in China. The results demonstrated that the Bayesian AR model had varying degrees of decline in the MAPE value in the seven sets of experimental data. The reductions ranged from 0.1431% to 0.0342%, indicating effective optimization of the Bayesian inference in the AR model parameters and reflecting the useful correction of the lag order adjustment strategy. The proposed Bayesian AR framework showed high accuracy index and stable prediction accuracy, thereby outperforming the traditional AR model.

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“…ARIMA and its seasonal counterpart SARIMA is found in numerous studies e.g., [6,12,13,[17][18][19] to be well suited to tackle problems like the 1-2 day ahead prediction of some of the weather variables of interest. In order to be useful in more applications with larger prediction horizons and more chaotic weather variables, combinations and hybridizations based on ML and SARIMA are suggested by authors e.g., [19][20][21][22][23][24] and the references therein. The task of creating robust and reliable hybrid techniques that improve the accuracy of time series prediction algorithms is one and maybe the most researched question in the field and it will be discussed more in depth in the methodology and discussion section later on.…”

Section: Introductionmentioning

confidence: 99%

Predicting Climatic Variables on Local Level with SARIMA, LSTM and Hybrid Techniques

Parasyris¹,

Alexandrakis²,

Kozyrakis³

et al. 2022

Preprint

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The choice of holiday destinations is highly depended on climate considerations. Nowadays, since the effects of climate crisis are being increasingly felt, the need of accurate weather and climate services for hotels is crucial. Such a service could be beneficial for both the future planning of tourists’ activities and destinations and for hotel managers as it could help in decision making about the planning and expansion of the touristic season, due to a prediction of higher temperatures for a longer time span, thus causing increased revenue for companies in the local touristic sector. The aim of this work is to calculate predictions on climatic variables using statistical techniques as well as Artificial Intelligence (AI) for a specific area of interest utilising data from in situ meteorological station, and produce valuable and reliable localised predictions with the most cost-effective method possible. This investigation will answer the question of the most suitable prediction method for time series data from a single meteorological station that is deployed in a specific location. As a result, an accurate representation of the microclimate in a specific are is achieved. To achieve this high accuracy in situ measurements and prediction techniques are used. As prediction techniques, Seasonal Auto Regressive Integrated Moving Average (SARIMA), AI techniques like the Long-Short-Term-Memory (LSTM) Neural Network and hybrid combinations of the two are used. Variables of interest are divided in the easier to predict temperature and humidity that are more periodic and less chaotic, and the wind speed as an example of a more stochastic variable with no known seasonality and patterns. Our results show that the examined Hybrid methodology performs the best at temperature and wind speed forecasts, closely followed by the SARIMA whereas LSTM perform better overall at the humidity forecast, even after the correction of the Hybrid to the SARIMA model.

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A hybrid ARIMA-LSTM model optimized by BP in the forecast of outpatient visits

Cited by 27 publications

References 31 publications

Forecast Methods for Time Series Data: A Survey

Forecast Methods for Time Series Data: A Survey

A Method for Improving the Prediction of Outpatient Visits for Hospital Management: Bayesian Autoregressive Analysis

Predicting Climatic Variables on Local Level with SARIMA, LSTM and Hybrid Techniques

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