Name
Title | ||
|---|---|---|
Recursive and Rolling Windows for Medical Time Series Forecasting: A Comparative Study |
Abstract | ||
|---|---|---|
Medical data accuracy is crucial for providing reliable remote healthcare services for patients. To this end, data accuracy assessment mechanisms must be developed. Prediction algorithms may be used in order to address this issue. This paper extends previous studies by investigating the relevance of different statistical concepts in modeling and predicting the variance instability of medical data. Doing this, we refer to the standard auto-regressive model as a benchmark for prediction purpose and two competing specifications namely the rolling and the recursive windows. We evaluate their forecasting adequacy for medical time series in terms of prediction errors and the Theil Inequality Coefficient. Results show on the one hand that the rolling window concept seems to be an efficient technique for forecasting medical series with instability variances. On the other hand, the recursive window performs better when forecasting medical time series with constant variances. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CSE-EUC-DCABES.2016.169 | 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES) |
Keywords | Field | DocType |
Medical data,forecasting,Auto-regressive,rolling window,recursive window | Data accuracy,Time series,Data modeling,Data mining,Computer science,Prediction algorithms,Artificial intelligence,Recursion,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5090-3594-6 | 0 | 0.34 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lamia Ben Amor | 1 | 6 | 3.15 |
| Imene Lahyani | 2 | 23 | 7.05 |
| Mohamed Jmaiel | 3 | 668 | 110.41 |