Time Series Data Analysis and Fault Diagnosis of Plant Process Equipment Using Statistical Machine Learning Method

2018 ◽  
Vol 23 (3) ◽  
pp. 193-201
Author(s):  
Se-Yun Hwang ◽  
Jeeyeon Heo ◽  
Kyu-Tack Hong ◽  
Jang-Hyun Lee
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Process Equipment ◽  
Machine Learning Method ◽  
Learning Method ◽  
Statistical Machine Learning ◽  
Time Series Data Analysis ◽  
Plant Process

scholarly journals A versatile computational algorithm for time-series data analysis and machine-learning models

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Taylor Chomiak ◽  
Neilen P. Rasiah ◽  
Leonardo A. Molina ◽  
Bin Hu ◽  
Jaideep S. Bains ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Computational Algorithm ◽  
Time Series Data ◽  
Simulated Data ◽  
Series Data ◽  
Learning Models ◽  
Machine Learning Model ◽  
Time Series Data Analysis

AbstractHere we introduce Local Topological Recurrence Analysis (LoTRA), a simple computational approach for analyzing time-series data. Its versatility is elucidated using simulated data, Parkinsonian gait, and in vivo brain dynamics. We also show that this algorithm can be used to build a remarkably simple machine-learning model capable of outperforming deep-learning models in detecting Parkinson’s disease from a single digital handwriting test.

Time series data analysis of stock price movement using machine learning techniques

2020 ◽  
Vol 24 (21) ◽  
pp. 16509-16517
Author(s):  
Irfan Ramzan Parray ◽  
Surinder Singh Khurana ◽  
Munish Kumar ◽  
Ali A. Altalbe
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Data Analysis ◽  
Stock Price ◽  
Time Series Data ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Price Movement ◽  
Learning Techniques ◽  
Time Series Data Analysis

scholarly journals Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262131
Author(s):  
Adil Aslam Mir ◽  
Kimberlee Jane Kearfott ◽  
Fatih Vehbi Çelebi ◽  
Muhammad Rafique
Keyword(s):  
Machine Learning ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Series Data ◽  
Machine Learning Method ◽  
Learning Method ◽  
Imputation Methods ◽  
Squared Error ◽  
Feature Importance

A new methodology, imputation by feature importance (IBFI), is studied that can be applied to any machine learning method to efficiently fill in any missing or irregularly sampled data. It applies to data missing completely at random (MCAR), missing not at random (MNAR), and missing at random (MAR). IBFI utilizes the feature importance and iteratively imputes missing values using any base learning algorithm. For this work, IBFI is tested on soil radon gas concentration (SRGC) data. XGBoost is used as the learning algorithm and missing data are simulated using R for different missingness scenarios. IBFI is based on the physically meaningful assumption that SRGC depends upon environmental parameters such as temperature and relative humidity. This assumption leads to a model obtained from the complete multivariate series where the controls are available by taking the attribute of interest as a response variable. IBFI is tested against other frequently used imputation methods, namely mean, median, mode, predictive mean matching (PMM), and hot-deck procedures. The performance of the different imputation methods was assessed using root mean squared error (RMSE), mean squared log error (MSLE), mean absolute percentage error (MAPE), percent bias (PB), and mean squared error (MSE) statistics. The imputation process requires more attention when multiple variables are missing in different samples, resulting in challenges to machine learning methods because some controls are missing. IBFI appears to have an advantage in such circumstances. For testing IBFI, Radon Time Series Data (RTS) has been used and data was collected from 1st March 2017 to the 11th of May 2018, including 4 seismic activities that have taken place during the data collection time.

Development of A Drug Early Warning System Model for Cardiac Arrest Using Deep Learning: Retrospective Cohort Study (Preprint)

2020 ◽  
Author(s):  
Hsiao-Ko Chang ◽  
Hui-Chih Wang ◽  
Chih-Fen Huang ◽  
Feipei Lai
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Cardiac Arrest ◽  
Early Warning ◽  
Time Series Data ◽  
Predictive Accuracy ◽  
Vital Signs ◽  
Warning System ◽  
Series Data ◽  
Dynamic Time

BACKGROUND In most of Taiwan’s medical institutions, congestion is a serious problem for emergency departments. Due to a lack of beds, patients spend more time in emergency retention zones, which make it difficult to detect cardiac arrest (CA). OBJECTIVE We seek to develop a Drug Early Warning System Model (DEWSM), it included drug injections and vital signs as this research important features. We use it to predict cardiac arrest in emergency departments via drug classification and medical expert suggestion. METHODS We propose this new model for detecting cardiac arrest via drug classification and by using a sliding window; we apply learning-based algorithms to time-series data for a DEWSM. By treating drug features as a dynamic time-series factor for cardiopulmonary resuscitation (CPR) patients, we increase sensitivity, reduce false alarm rates and mortality, and increase the model’s accuracy. To evaluate the proposed model, we use the area under the receiver operating characteristic curve (AUROC). RESULTS Four important findings are as follows: (1) We identify the most important drug predictors: bits (intravenous therapy), and replenishers and regulators of water and electrolytes (fluid and electrolyte supplement). The best AUROC of bits is 85%, it means the medical expert suggest the drug features: bits, it will affect the vital signs, and then the evaluate this model correctly classified patients with CPR reach 85%; that of replenishers and regulators of water and electrolytes is 86%. These two features are the most influential of the drug features in the task. (2) We verify feature selection, in which accounting for drugs improve the accuracy: In Task 1, the best AUROC of vital signs is 77%, and that of all features is 86%. In Task 2, the best AUROC of all features is 85%, which demonstrates that thus accounting for the drugs significantly affects prediction. (3) We use a better model: For traditional machine learning, this study adds a new AI technology: the long short-term memory (LSTM) model with the best time-series accuracy, comparable to the traditional random forest (RF) model; the two AUROC measures are 85%. It can be seen that the use of new AI technology will achieve better results, currently comparable to the accuracy of traditional common RF, and the LSTM model can be adjusted in the future to obtain better results. (4) We determine whether the event can be predicted beforehand: The best classifier is still an RF model, in which the observational starting time is 4 hours before the CPR event. Although the accuracy is impaired, the predictive accuracy still reaches 70%. Therefore, we believe that CPR events can be predicted four hours before the event. CONCLUSIONS This paper uses a sliding window to account for dynamic time-series data consisting of the patient’s vital signs and drug injections. The National Early Warning Score (NEWS) only focuses on the score of vital signs, and does not include factors related to drug injections. In this study, the experimental results of adding the drug injections are better than only vital signs. In a comparison with NEWS, we improve predictive accuracy via feature selection, which includes drugs as features. In addition, we use traditional machine learning methods and deep learning (using LSTM method as the main processing time series data) as the basis for comparison of this research. The proposed DEWSM, which offers 4-hour predictions, is better than the NEWS in the literature. This also confirms that the doctor’s heuristic rules are consistent with the results found by machine learning algorithms.

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