Faster Healthcare Time Series Classification for Boosting Mortality Early Warning System

Penelitian ini bertujuan untuk menganalisis model sistem deteksi dini (early warning system/EWS) pada perbankan Syariah. Data dalam penelitian ini berbentuk time series bulanan dari bulan Januari 2004 sampai bulan Desember 2016. Indikator dependen dalam penelitian ini adalah indeks ketahanan perbankan syariah (Syariah banking robustness index), indikator dependen ini dibentuk melalui dua komponen yaitu dana pihak ketiga (DPK) dan pembiayaan perbankan Syariah. Sedangkan indikator independen yaitu non performing financing (NPF), financing deposit to ratio (FDR), inflasi, produk domestik bruto (PDB) dan suku bunga. Hasil penelitian menunjukkan berdasarkan indeks ketahanan perbankan Syariah (Syariah banking robustness index), terdapat ketahanan yang buruk pada perbankan Syariah di tahun 2004 dan 2005. Ketahanan yang buruk ini lebih disebabkan oleh faktor internal perbankan. Selain itu, berdasarkan 5 indikator yang digunakan, hanya 3 indikator yang dapat menjadi leading indicators yaitu suku bunga, inflasi dan financing to deposit ratio (FDR). Tiga leading indicators didapatkan berdasarkan penilaian berbagai kriteria, salah satunya adalah noise to signal ratio (NSR). Langkah selanjutnya adalah mengolah 3 leading indicators dengan logit. Hasil dengan logit menunjukkan dari 3 leading indicators,hanya suku bunga yang berpengaruh signifikan dan nilai ods ratio leading indicator suku bunga sebesar 79.29%. Kesimpulan dari penelitian ini adalah indikator suku bunga menjadi indikator yang sangat berpengaruh terhadap kinerja perbankan Syariah.

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Air Safety Early Warning System Based on Time Series

2019 IEEE 1st International Conference on Civil Aviation Safety and Information Technology (ICCASIT) ◽

10.1109/iccasit48058.2019.8973191 ◽

2019 ◽

Author(s):

Weiwei Zhang ◽

Qing Li ◽

Hang Yu

Keyword(s):

Time Series ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Air Safety

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Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model

Scientific Reports ◽

10.1038/s41598-019-53838-3 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Yoonhee Kim ◽

J. V. Ratnam ◽

Takeshi Doi ◽

Yushi Morioka ◽

Swadhin Behera ◽

...

Keyword(s):

Time Series ◽

Prediction Model ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Malaria Surveillance ◽

Short Term ◽

Seasonal Climate ◽

Climate Forecasts ◽

Seasonal Climate Forecasts

AbstractAlthough there have been enormous demands and efforts to develop an early warning system for malaria, no sustainable system has remained. Well-organized malaria surveillance and high-quality climate forecasts are required to sustain a malaria early warning system in conjunction with an effective malaria prediction model. We aimed to develop a weather-based malaria prediction model using a weekly time-series data including temperature, precipitation, and malaria cases from 1998 to 2015 in Vhembe, Limpopo, South Africa and apply it to seasonal climate forecasts. The malaria prediction model performed well for short-term predictions (correlation coefficient, r > 0.8 for 1- and 2-week ahead forecasts). The prediction accuracy decreased as the lead time increased but retained fairly good performance (r > 0.7) up to the 16-week ahead prediction. The demonstration of the malaria prediction process based on the seasonal climate forecasts showed the short-term predictions coincided closely with the observed malaria cases. The weather-based malaria prediction model we developed could be applicable in practice together with skillful seasonal climate forecasts and existing malaria surveillance data. Establishing an automated operating system based on real-time data inputs will be beneficial for the malaria early warning system, and can be an instructive example for other malaria-endemic areas.

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An Early Warning System for Flood Detection Using Critical Slowing Down

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17176131 ◽

2020 ◽

Vol 17 (17) ◽

pp. 6131

Author(s):

Syed Mohamad Sadiq Syed Musa ◽

Mohd Salmi Md Noorani ◽

Fatimah Abdul Razak ◽

Munira Ismail ◽

Mohd Almie Alias ◽

...

Keyword(s):

Time Series ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Warning Signal ◽

Quantile Estimation ◽

Flood Events ◽

Slowing Down ◽

Level Data ◽

Water Level Data

The theory of critical slowing down (CSD) suggests an increasing pattern in the time series of CSD indicators near catastrophic events. This theory has been successfully used as a generic indicator of early warning signals in various fields, including climate research. In this paper, we present an application of CSD on water level data with the aim of producing an early warning signal for floods. To achieve this, we inspect the trend of CSD indicators using quantile estimation instead of using the standard method of Kendall’s tau rank correlation, which we found is inconsistent for our data set. For our flood early warning system (FLEWS), quantile estimation is used to provide thresholds to extract the dates associated with significant increases on the time series of the CSD indicators. We apply CSD theory on water level data of Kelantan River and found that it is a reliable technique to produce a FLEWS as it demonstrates an increasing pattern near the flood events. We then apply quantile estimation on the time series of CSD indicators and we manage to establish an early warning signal for ten of the twelve flood events. The other two events are detected on the first day of the flood.

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