Empirical Simultaneous Prediction Regions for Path-Forecasts

Simultaneous prediction regions

Probability Theory and Related Fields ◽

10.1007/bf01194922 ◽

1992 ◽

Vol 92 (2) ◽

pp. 231-246 ◽

Cited By ~ 1

Author(s):

Rudolf Beran

Keyword(s):

Simultaneous Prediction ◽

Prediction Regions

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Empirical simultaneous prediction regions for path-forecasts

International Journal of Forecasting ◽

10.1016/j.ijforecast.2012.12.002 ◽

2013 ◽

Vol 29 (3) ◽

pp. 456-468 ◽

Cited By ~ 6

Author(s):

Òscar Jordà ◽

Malte Knüppel ◽

Massimiliano Marcellino

Keyword(s):

Simultaneous Prediction ◽

Prediction Regions

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Combined deep CNN–LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features

Scientific Reports ◽

10.1038/s41598-021-92997-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Da Un Jeong ◽

Ki Moo Lim

Keyword(s):

Blood Pressure ◽

Blood Pressure Measurement ◽

Patient Specific ◽

Peak Time ◽

Simultaneous Prediction ◽

Deep Cnn ◽

Pressure Estimation ◽

The Difference ◽

Blood Pressure Estimation ◽

Continuous Blood Pressure

AbstractThe pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices.

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Bootstrap joint prediction regions for sequences of missing values in spatio-temporal datasets

Computational Statistics ◽

10.1007/s00180-021-01099-y ◽

2021 ◽

Author(s):

Maria Lucia Parrella ◽

Giuseppina Albano ◽

Cira Perna ◽

Michele La Rocca

Keyword(s):

Missing Data ◽

Missing Values ◽

Sample Selection ◽

Sample Path ◽

Temporal Relationships ◽

Empirical Performance ◽

Spatio Temporal ◽

Joint Prediction ◽

Point Forecast ◽

Prediction Regions

AbstractMissing data reconstruction is a critical step in the analysis and mining of spatio-temporal data. However, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. To take into account the uncertainty in the point forecast, some prediction intervals may be of interest. In particular, for (possibly long) missing sequences of consecutive time points, joint prediction regions are desirable. In this paper we propose a bootstrap resampling scheme to construct joint prediction regions that approximately contain missing paths of a time components in a spatio-temporal framework, with global probability $$1-\alpha $$ 1 - α . In many applications, considering the coverage of the whole missing sample-path might appear too restrictive. To perceive more informative inference, we also derive smaller joint prediction regions that only contain all elements of missing paths up to a small number k of them with probability $$1-\alpha $$ 1 - α . A simulation experiment is performed to validate the empirical performance of the proposed joint bootstrap prediction and to compare it with some alternative procedures based on a simple nominal coverage correction, loosely inspired by the Bonferroni approach, which are expected to work well standard scenarios.

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