scholarly journals Statistical pattern detection in univariate time series of intensive care on-line monitoring data

1998 ◽  
Vol 24 (12) ◽  
pp. 1305-1314 ◽  
Author(s):  
M. Imhoff ◽  
M. Bauer ◽  
U. Gather ◽  
D. Löhlein
Keyword(s):  
Time Series ◽  
Intensive Care ◽  
Pattern Detection ◽  
Monitoring Data ◽  
Univariate Time Series ◽  
Statistical Pattern ◽  
On Line

Analysis on Continuous Monitoring Data for ICU Based on On-Line Segmentation Algorithm

2013 ◽  
Vol 427-429 ◽  
pp. 1489-1492
Author(s):  
Fang Wang
Keyword(s):  
Time Series ◽  
Continuous Monitoring ◽  
Evaluation Method ◽  
Time Series Data ◽  
Segmentation Algorithm ◽  
Monitoring Data ◽  
Series Data ◽  
Recursion Method ◽  
On Line ◽  
Line Segmentation

It is all kinds of data monitoring for ICU that are very important, on the one hand, it can provide reliable reference for medical personnel, so that they can care for critical patients in time, on the other hand, it also can avoid bringing trouble which is caused by instrument to severe patients. Through the mining technology of time series data ,this paper uses online segmentation algorithm of time series, establishing continuous monitoring data model for ICU and creating a time series Table, from the data of which, it can quickly extract monitoring data, and do real-time analysis. On this basis, this paper also puts forward an evaluation method for on-line segmentation algorithm performance , and also puts forward a kind of algorithm to speed up the time sequence segmentation recursion method, which can quickly extract the key components in the data, so as to accelerate the analysis on continuous monitoring data . Finally, through the continuous monitoring and analysis on the pressure of the severe patients who are inserted artificial airway balloon, this paper tests the reliability of the algorithm, and through the analysis and comparison with the data, it proves the quickness of algorithm, and provides a theoretical basis for analysis on continuous monitoring data for ICU.

On-Line Structural Damage Feature Extraction Based on Autoregressive Statistical Pattern of Time Series

2014 ◽  
Author(s):  
Ming-Hui Hu ◽  
Shan-Tung Tu ◽  
Fu-Zhen Xuan ◽  
Zheng-Dong Wang
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Structural Damage ◽  
Power Plants ◽  
Damage Identification ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Statistical Pattern ◽  
On Line ◽  
The Relationship

The main aim of this paper is to demonstrate an autoregressive statistical pattern analysis method for the on-line structural health monitoring based on the damage feature extraction. The strain signals obtained from sensors are modeled as autoregressive moving average (ARMA) time series to extract the damage sensitive features (DSF) to monitor the variations of the selected features. One algebra combination of the first three AR coefficients is defined as damage sensitive feature. Using simple theory of polynomial roots, the relationship between the first three AR coefficient and the roots of the characteristic equation of the transfer function is deduced. Structural damage detection is conducted by comparing the DSF values of the inspected structure. The corresponding damage identification experiment was investigated in X12CrMoWVNbN steel commonly used for rotor of steam turbine in power plants. The feasibility and validity of the proposed method are shown.

Symbolic Time-Series Analysis of Gas Turbine Gas Path Electrostatic Monitoring Data

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Jianzhong Sun ◽  
Pengpeng Liu ◽  
Yibing Yin ◽  
Hongfu Zuo ◽  
Chaoyi Li
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Monitoring Data ◽  
Data Set ◽  
Series Analysis ◽  
Path Component ◽  
On Line ◽  
Signal Sample ◽  
Specific Operating Condition ◽  
Symbolic Time Series Analysis

The aero-engine gas-path electrostatic monitoring system is capable of providing early warning of impending gas-path component faults. In the presented work, a method is proposed to acquire signal sample under a specific operating condition for on-line fault detection. The symbolic time-series analysis (STSA) method is adopted for the analysis of signal sample. Advantages of the proposed method include its efficiency in numerical computations and being less sensitive to measurement noise, which is suitable for in situ engine health monitoring application. A case study is carried out on a data set acquired during a turbojet engine reliability test program. It is found that the proposed symbolic analysis techniques can be used to characterize the statistical patterns presented in the gas path electrostatic monitoring data (GPEMD) for different health conditions. The proposed anomaly measure, i.e., the relative entropy derived from the statistical patterns, is confirmed to be able to indicate the gas path components faults. Finally, the further research task and direction are discussed.

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

scholarly journals A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 141
Author(s):  
Jacob Hale ◽  
Suzanna Long
Keyword(s):  
Time Series ◽  
Electricity Generation ◽  
Sustainability Assessment ◽  
Moving Average ◽  
Time Series Prediction ◽  
State Level ◽  
Autoregressive Integrated Moving Average ◽  
Energy Transitions ◽  
Univariate Time Series ◽  
Partial Energy

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

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