Using time series analysis to predict affecting factors of thallium myocardial perfusion scan service usage frequency

Lin, Weir-Sen; Lin, Ching-Yuan; Wu, Ching-Hsuan

doi:10.1109/icasi.2018.8394282

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…Moreover, its horizontal resolution is about 7.5 km over France and 37 km in the antipodes. ARPEGE has 105 levels where the first level is 10 meters above the surface and can reach 11 km of altitude [8] [9] [10].…”

Section: Arpegementioning

confidence: 99%

“…Ljung-Box statistical test should be applied to check if the residuals are considered as white noise [10]. Based on Table 2 In fact the model was built on all the data from 01/01/2006 to 31/12/2016 and can predict five days of January 2019 but to predict August 2016 the proposed model had been built on data from 01/01/2006 to 31/07/2016 is more usual.…”

Section: ) Seasonality Verificationmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Methodology for Short Term Temperature Forecasting

Abdallah¹,

Abdallah²,

Marion³

et al. 2020

IJIS

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Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction model used by the civil aviation weather service at Rafic Hariri International Airport in Beirut (BRHIA) is the ARPEGE model, (0.5) developed by the weather service in France. Unfortunately, forecasts provided by ARPEGE have been erroneous and biased by several factors such as the chaotic character of the physical modeling equations of some atmospheric phenomena (advection, convection, etc.) and the nature of the Lebanese topography. In this paper, we proposed the time series method ARIMA (Auto Regressive Integrated Moving Average) to forecast the minimum daily temperature and compared its result with ARPEGE. As a result, ARIMA method shows better mean accuracy (91%) over the numerical model ARPEGE (68%), for the prediction of five days in January 2017. Moreover, back to five months ago, in order to validate the accuracy of the proposed model, a simulation has been applied on the first five days of August 2016. Results have shown that the time series ARIMA method has offered better mean accuracy (98%) over the numerical model ARPEGE (89%) for the prediction of five days of August 2016. This paper discusses a multiprocessing approach applied to ARIMA in order to enhance the efficiency of ARIMA in terms of complexity and resources.

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Section: Arpegementioning

confidence: 99%

Section: ) Seasonality Verificationmentioning

confidence: 99%

A Hybrid Methodology for Short Term Temperature Forecasting

Abdallah¹,

Abdallah²,

Marion³

et al. 2020

IJIS

View full text Add to dashboard Cite

show abstract

“…After defining the SARIMA model coefficients, the ACF and PACF of the model residuals were displayed to ensure that they did not fall outside the confidence interval. Then, the Ljung-Box test was applied to check if the residuals were white noise or not [20]. The p values obtained are all greater than 0.05 significant, implying that the residuals are white noise and therefore our model is valid.…”

Section: Contributionmentioning

confidence: 99%

A vector autoregressive methodology for short-term weather forecasting: tests for Lebanon

Abdallah

Marion

et al. 2020

SN Appl. Sci.

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Weather forecasting has been a major challenge due to the uncertain nature of the weather. Numerical models, such as the "Action de Recherche Petite Echelle Grande Echelle" (ARPEGE), the "Global Forecasting System", the "European Center for Medium-Range Weather Forecasts", are widely adopted by many meteorological services to forecast weather parameters. Under certain conditions, numerical models may have lower forecast accuracy, which is due to several factors such as the chaotic nature of the partial differential equations that simulate the evolution of the atmosphere and the difficulty of forecasting over countries with a fairly small surface area but with a very varied relief. This paper proposes a time series analysis approach based on the vector autoregression model (VAR) as an alternative and robust solution. The results are very promising (average of about 96.67% of precision between real values and three predicted parameters: temperature minimum, maximum humidity and precipitation) in the field of short-term weather parameter forecasting. In addition, the use of VAR models has solved the major problem posed by the chaotic equations of the ARPEGE model with greater accuracy on the one hand, and the execution time, forecasting accuracy and robustness of the SARIMA univariate models on the other.

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Using time series analysis to predict affecting factors of thallium myocardial perfusion scan service usage frequency

Cited by 2 publications

References 12 publications

A Hybrid Methodology for Short Term Temperature Forecasting

A Hybrid Methodology for Short Term Temperature Forecasting

A vector autoregressive methodology for short-term weather forecasting: tests for Lebanon

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