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Precision and Recall for Time Series
[article]
2019
arXiv
pre-print
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Our model expands the well-known Precision and Recall metrics to measure ranges, while simultaneously enabling customization support for domain-specific preferences. ...
Motivated by this observation, we present a new mathematical model to evaluate the accuracy of time series classification algorithms. ...
Acknowledgments We thank Eric Metcalf for his help with experiments. This research has been funded in part by Intel and by NSF grant IIS-1526639. ...
arXiv:1803.03639v3
fatcat:w7ullujqcvamzagalxh7va6r7i
Model-based search in large time series databases
2011
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments - PETRA '11
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such sensors over large time intervals. ...
In particular, we describe the two different approaches, and we identify some important pros and cons for each approach. ...
to Professors Chris Ding and Fillia Makedon. ...
doi:10.1145/2141622.2141666
dblp:conf/petra/KotsifakosAPHG11
fatcat:br7yeelpsrfkzpztotkntjndf4
Estimating the Best Time to View Cherry Blossoms Using Time-Series Forecasting Method
2022
Machine Learning and Knowledge Extraction
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This study proposes a time-series prediction method using SNS data and machine learning as a new method for estimating the best times for viewing for a certain period. ...
Combining the time-series forecasting method and the low-cost moving average method yields an estimate of the best time to view cherry blossoms. ...
data, and by the values of recall and precision. ...
doi:10.3390/make4020018
fatcat:7un42nag7vhnfd2jcom6mglw6i
Benchmarking Deep Learning Interpretability in Time Series Predictions
[article]
2020
arXiv
pre-print
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We propose and report multiple metrics to empirically evaluate the performance of saliency methods for detecting feature importance over time using both precision (i.e., whether identified features contain ...
These methods are mostly used in vision and language tasks, and their applications to time series data is relatively unexplored. ...
Acknowledgements We thank Kalinda Vathupola for his thoughtful feedback on this work. ...
arXiv:2010.13924v1
fatcat:4c7hmjrgfjchdb6b4py22yspey
Automatic SARIMA Order Identification Convolutional Neural Network
2020
International Journal of Machine Learning and Computing
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For the time series models such as the autoregressive integrated moving average (ARIMA) model and the seasonal autoregressive integrated moving average (SARIMA) model, statisticians mostly identify the ...
The performance of the ASOC model provides better performance than the likelihood method via precision, recall and f1-score. ...
Fig. 7 demonstrates precision, recall and f1-score between model A1 and model C which it is clear that model A1 gives the best precision, recall and f1-score.
C. ...
doi:10.18178/ijmlc.2020.10.5.988
fatcat:tuya5tuslbfspfxv4zsb4sdn6u
Precision and Recall for Range-Based Anomaly Detection
[article]
2018
arXiv
pre-print
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In this paper, we present a new mathematical model to express range-based anomalies, anomalies that occur over a range (or period) of time. ...
Precision and Recall for Range-Based Anomaly Detection SysML'18, February 2018, Stanford, CA, USA ...
correctness for domain-specific time-series anomalies. ...
arXiv:1801.03175v3
fatcat:ragils3mqfa5depcm4aizzh3eq
Using Relevance Feedback to Learn Both the Distance Measure and the Query in Multimedia Databases
[chapter]
2005
Lecture Notes in Computer Science
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system and query refinement can further improve the precision/recall by a wide margin. ...
We demonstrate utility of our approach on both classification and query retrieval tasks for time series and other types of multimedia data, then show that its incorporating into the relevance feedback ...
the trade off between efficiency and precision/recall. ...
doi:10.1007/11552451_3
fatcat:7be63scew5ca3albix4b3nuwua
Unsupervised Anomaly Detection Approach for Time-Series in Multi-Domains Using Deep Reconstruction Error
2020
Symmetry
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We conducted two types of experiments on a total of 52 publicly available time-series benchmark datasets for the batch and real-time anomaly detections. ...
Automatic anomaly detection for time-series is critical in a variety of real-world domains such as fraud detection, fault diagnosis, and patient monitoring. ...
We compared the precision, recall, and F-measure of the RE-ADTS to the evaluation of six algorithms on 20 time-series datasets in [20] . ...
doi:10.3390/sym12081251
fatcat:bm5cjz7775f5tnmjjfphvdldca
Learning stochastic finite-state transducer to predict individual patient outcomes
2016
Health and Technology
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The high frequency data in intensive care unit is flashed on a screen for a few seconds and never used again. ...
In this paper, we learned the edit distance costs of a symbolic univariate time series representation through a stochastic finite-state transducer to predict patient outcomes in intensive care units. ...
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ...
doi:10.1007/s12553-016-0146-2
pmid:27942425
pmcid:PMC5124435
fatcat:6yqc64yo4vejxb4tvxfxen2oyu
An approach for dynamical network reconstruction of simple network motifs
2013
BMC Systems Biology
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As a result, our method could reconstruct about 40% of interactions in network motif from time-series data set. ...
Moreover the introduction of time-series data of one-factor disrupted model could remarkably improved the performance of network inference. ...
Acknowledgements This work was partly supported by the commission for Development of Artificial Gene Synthesis Technology for Creating Innovative Biomaterial from the Ministry of Economy, Trade and Industry ...
doi:10.1186/1752-0509-7-s6-s4
pmid:24564905
pmcid:PMC4029519
fatcat:l6dicttqyre57gl4r5ya2ynlwu
Granger Causality for Time-Series Anomaly Detection
2012
2012 IEEE 12th International Conference on Data Mining
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Recent developments in industrial systems provide us with a large amount of time series data from sensors, logs, system settings and physical measurements, etc. ...
However, the special characteristics of these time series data, such as high dimensions and complex dependencies between variables, as well as its massive volume, pose great challenges to existing anomaly ...
1 = 2 · Recall · Precision/(Recall + Precision)) of all eight methods. ...
doi:10.1109/icdm.2012.73
dblp:conf/icdm/QiuLSL12
fatcat:qf2xrcisezdwvgxrjuaftwnucm
All-Clear Flare Prediction Using Interval-based Time Series Classifiers
[article]
2021
arXiv
pre-print
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2020
2020 IEEE International Conference on Big Data (Big Data)
unpublished
doi:10.1109/bigdata50022.2020.9377906 fatcat:ztsat7nupzgwrit5s6qign52rm
doi:10.1109/bigdata50022.2020.9377906 fatcat:ztsat7nupzgwrit5s6qign52rm
Our results show that time series classifiers provide better forecasting results in terms of skill scores, precision and recall metrics, and they can be further improved for more precise all-clear forecasts ...
Our study focuses on training and testing a set of interval-based time series classifiers named Time Series Forest (TSF). ...
Results are taken from undersampled datasets (Partition 1 and 2 for Base Learners and Partition 4 for Meta-learner) Precision(XM) TPR/Recall(XM) Precision(CBN) TNR/Recall(CBN) The precision and recall ...
arXiv:2105.01202v1
fatcat:yjrtzspqprbhvnbqt6zqldably
Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling
[article]
2020
arXiv
pre-print
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2020
2020 International SAUPEC/RobMech/PRASA Conference
unpublished
doi:10.1109/saupec/robmech/prasa48453.2020.9078028 fatcat:zondbrco3jd5ledyf5xa4rurb4
doi:10.1109/saupec/robmech/prasa48453.2020.9078028 fatcat:zondbrco3jd5ledyf5xa4rurb4
Overall the GRU performs better for longer time series than the LSTM. ...
Through active fire analysis, it is possible to reproduce a dynamical process, such as wildfires, with limited duration time series data. ...
ACKNOWLEDGMENT I would like to thank Prof. van Zyl for all of his intellectual insight and motivation throughout. It was a great pleasure being supervised by him. ...
arXiv:2005.13040v1
fatcat:mos3pybpmvczplgqblcyrzahra
PSEUDO-MULTIVARIATE LSTM NEURAL NETWORK APPROACH FOR PURCHASE DAY PREDICTION IN B2B
2021
Facta Universitatis Series Automatic Control and Robotics
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This process was then repeated for the entire six month period and a slight downward trend can be noticed for error metrics, leading to the conclusion that the network would perform even better over time ...
By aggregating purchase data for all products a customer purchased, we were able to get more precise predictions of the next purchase. ...
For these two classes precision and recall were calculated and then compared to earlier results. ...
doi:10.22190/fuacr2003151c
fatcat:rc4xelcqmrdnhh7zfgel3rmqqi
Statistical Evaluation of Anomaly Detectors for Sequences
[article]
2020
arXiv
pre-print
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In this work, we formalize a notion of precision and recall with temporal tolerance for point-based anomaly detection in sequential data. ...
Although precision and recall are standard performance measures for anomaly detection, their statistical properties in sequential detection settings are poorly understood. ...
They introduced novel precision and recall measures for range-based anomaly detection. ...
arXiv:2008.05788v1
fatcat:r3zbmawqkzhzxpsxxctpfet5hm
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