相关论文
2021 - Journal of Data Science
Singular Spectrum Analysis: Methodology and Comparison

In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, the performance of the SSA tech- nique has been considered by applying it to a well-known time series data set, namely, monthly accidental deaths in the USA. The results are com- pared with those obtained using Box-Jenkins SARIMA models, the ARAR algorithm and the Holt-Winter algorithm (as described in Brockwell and Davis (2002)). The results show that the SSA technique gives a much more accurate forecast than the other methods indicated above.

2008 - World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering
A Hidden Markov Model-Based Isolated and Meaningful Hand Gesture Recognition

Gesture recognition is a challenging task for extracting meaningful gesture from continuous hand motion. In this paper, we propose an automatic system that recognizes isolated gesture, in addition meaningful gesture from continuous hand motion for Arabic numbers from 0 to 9 in real-time based on Hidden Markov Models (HMM). In order to handle isolated gesture, HMM using Ergodic, Left-Right (LR) and Left-Right Banded (LRB) topologies is applied over the discrete vector feature that is extracted from stereo color image sequences. These topologies are considered to different number of states ranging from 3 to 10. A new system is developed to recognize the meaningful gesture based on zero-codeword detection with static velocity motion for continuous gesture. Therefore, the LRB topology in conjunction with Baum-Welch (BW) algorithm for training and forward algorithm with Viterbi path for testing presents the best performance. Experimental results show that the proposed system can successfully recognize isolated and meaningful gesture and achieve average rate recognition 98.6% and 94.29% respectively.

2001 - Studies in health technology and informatics
Objective laparoscopic skills assessments of surgical residents using Hidden Markov Models based on haptic information and tool/tissue interactions.

UNLABELLED Laparoscopic surgical skills evaluation of surgery residents is usually a subjective process, carried out in the operating room by senior surgeons. By its nature, this process is performed using fuzzy criteria. The objective of the current study was to develop and assess an objective laparoscopic surgical skill scale using Hidden Markov Models (HMM) based on haptic information, tool/tissue interactions and visual task decomposition. METHODS Eight subjects (six surgical trainees: first year surgical residents 2 x R1, third year surgical residents 2 x R3 fifth year surgical residents 2 x R5; and two expert laparoscopic surgeons: 2 x ES) performed laparoscopic cholecystectomy following a specific 7 steps protocol on a pig. An instrumented laparoscopic grasper equipped with a three-axis force/torque sensor located at the proximal end with an additional force sensor located on the handle, was used to measure the forces and torques. The hand/tool interface force/torque data was synchronized with a video of the tool operative maneuvers. A synthesis of frame-by-frame video analysis was used to define 14 different types of tool/tissue interactions, each one associated with unique force/torque (F/T) signatures. HMMs were developed for each subject representing the surgical skills by defining the various tool/tissue interactions as states and the associated F/T signatures as observations. The statistical distance between the HMMs representing residents at different levels of their training and the HMMs of expert surgeons were calculated in order to generate a learning curve of selected steps during laparoscopic cholecystectomy. RESULTS Comparison of HMM's between groups showed significant differences between all skill levels, supporting the objective definition of a learning curve. The major differences between skill levels were: (i) magnitudes of F/T applied (ii) types of tool/tissue interactions used and the transition between them and (iii) time intervals spent in each tool/tissue interaction and the overall completion time. The objective HMM analysis showed that the greatest difference in performance was between R1 and R3 groups and then decreased as the level of expertise increased, suggesting that significant laparoscopic surgical capability develops between the first and the third years of their residency training. The power of the methodology using HMM for objective surgical skill assessment arises from the fact that it compiles enormous amount of data regarding different aspects of surgical skill into a very compact model that can be translated into a single number representing the distance from expert performance. Moreover, the methodology is not limited to in-vivo condition as demonstrated in the current study. It can be extended to other modalities such as measuring performance in surgical simulators and robotic systems.

2005 - Data Mining and Knowledge Discovery Handbook
Mining Time Series Data

Much of the world’s supply of data is in the form of time series. In the last decade, there has been an explosion of interest in mining time series data. A number of new algorithms have been introduced to classify, cluster, segment, index, discover rules, and detect anomalies/novelties in time series. While these many different techniques used to solve these problems use a multitude of different techniques, they all have one common factor; they require some high level representation of the data, rather than the original raw data. These high level representations are necessary as a feature extraction step, or simply to make the storage, transmission, and computation of massive dataset feasible. A multitude of representations have been proposed in the literature, including spectral transforms, wavelets transforms, piecewise polynomials, eigenfunctions, and symbolic mappings. This chapter gives a high-level survey of time series Data Mining tasks, with an emphasis on time series representations.

2004 - EURASIP Journal on Advances in Signal Processing
Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model

We developed a system that automatically authenticates offline handwritten signatures using the discrete Radon transform (DRT) and a hidden Markov model (HMM). Given the robustness of our algorithm and the fact that only global features are considered, satisfactory results are obtained. Using a database of 924 signatures from 22 writers, our system achieves an equal error rate (EER) of 18% when only high-quality forgeries (skilled forgeries) are considered and an EER of 4.5% in the case of only casual forgeries. These signatures were originally captured offline. Using another database of 4800 signatures from 51 writers, our system achieves an EER of 12.2% when only skilled forgeries are considered. These signatures were originally captured online and then digitally converted into static signature images. These results compare well with the results of other algorithms that consider only global features.

2012 - Nucleic Acids Research
Dfam: a database of repetitive DNA based on profile hidden Markov models

We present a database of repetitive DNA elements, called Dfam (http://dfam.janelia.org). Many genomes contain a large fraction of repetitive DNA, much of which is made up of remnants of transposable elements (TEs). Accurate annotation of TEs enables research into their biology and can shed light on the evolutionary processes that shape genomes. Identification and masking of TEs can also greatly simplify many downstream genome annotation and sequence analysis tasks. The commonly used TE annotation tools RepeatMasker and Censor depend on sequence homology search tools such as cross_match and BLAST variants, as well as Repbase, a collection of known TE families each represented by a single consensus sequence. Dfam contains entries corresponding to all Repbase TE entries for which instances have been found in the human genome. Each Dfam entry is represented by a profile hidden Markov model, built from alignments generated using RepeatMasker and Repbase. When used in conjunction with the hidden Markov model search tool nhmmer, Dfam produces a 2.9% increase in coverage over consensus sequence search methods on a large human benchmark, while maintaining low false discovery rates, and coverage of the full human genome is 54.5%. The website provides a collection of tools and data views to support improved TE curation and annotation efforts. Dfam is also available for download in flat file format or in the form of MySQL table dumps.

2008 - ICML '08
Beam sampling for the infinite hidden Markov model

The infinite hidden Markov model is a non-parametric extension of the widely used hidden Markov model. Our paper introduces a new inference algorithm for the infinite Hidden Markov model called beam sampling. Beam sampling combines slice sampling, which limits the number of states considered at each time step to a finite number, with dynamic programming, which samples whole state trajectories efficiently. Our algorithm typically outperforms the Gibbs sampler and is more robust. We present applications of iHMM inference using the beam sampler on changepoint detection and text prediction problems.

1985 - ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing
Context-dependent modeling for acoustic-phonetic recognition of continuous speech

This paper describes the results of our work in designing a system for phonetic recognition of unrestricted continuous speech. We describe several algorithms used to recognize phonemes using context-dependent Hidden Markov Models of the phonemes. We present results for several variations of the parameters of the algorithms. In addition, we propose a technique that makes it possible to integrate traditional acoustic-phonetic features into a hidden Markov process. The categorical decisions usually associated with heuristic acoustic-phonetic algorithms are replaced by automated training techniques and global search strategies. The combination of general spectral information and specific acoustic-phonetic features is shown to result in more accurate phonetic recognition than either representation by itself.

1998
Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids

Probablistic models are becoming increasingly important in analyzing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analyzing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it is accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time presents the state of the art in this new and important field.

2011 - SDM
A Complexity-Invariant Distance Measure for Time Series

The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While there is a plethora of classification algorithms that can be applied to time series, all of the current empirical evidence suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping. In this work we make a surprising claim. There is an invariance that the community has missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where complex objects are incorrectly assigned to a simpler class. We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series classification experiments ever attempted, and show that complexity-invariant distance measures can produce improvements in accuracy in the vast majority of cases.

1996 - NIPS
Clustering Sequences with Hidden Markov Models

This paper discusses a probabilistic model-based approach to clustering sequences, using hidden Markov models (HMMs). The problem can be framed as a generalization of the standard mixture model approach to clustering in feature space. Two primary issues are addressed. First, a novel parameter initialization procedure is proposed, and second, the more difficult problem of determining the number of clusters K, from the data, is investigated. Experimental results indicate that the proposed techniques are useful for revealing hidden cluster structure in data sets of sequences.

1994 - Comput. Linguistics
Tagging English Text with a Probabilistic Model

In this paper we present some experiments on the use of a probabilistic model to tag English text, i.e. to assign to each word the correct tag (part of speech) in the context of the sentence. The main novelty of these experiments is the use of untagged text in the training of the model. We have used a simple triclass Markov model and are looking for the best way to estimate the parameters of this model, depending on the kind and amount of training data provided. Two approaches in particular are compared and combined:using text that has been tagged by hand and computing relative frequency counts,using text without tags and training the model as a hidden Markov process, according to a Maximum Likelihood principle.Experminents show that the best training is obtained by using as much tagged text as possible. They also show that Maximum Likelihood training, the procedure that is routinely used to estimate hidden Markov models parameters from training data, will not necessarily improve the tagging accuracy. In fact, it will generally degrade this accuracy, except when only a limited amount of hand-tagged text is available.

2001 - Int. J. Pattern Recognit. Artif. Intell.
An Introduction to Hidden Markov Models and Bayesian Networks

We provide a tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks. This perspective make sit possible to consider novel generalizations to hidden Markov models with multiple hidden state variables, multiscale representations, and mixed discrete and continuous variables. Although exact inference in these generalizations is usually intractable, one can use approximate inference in these generalizations is usually intractable, one can use approximate inference algorithms such as Markov chain sampling and variational methods. We describe how such methods are applied to these generalized hidden Markov models. We conclude this review with a discussion of Bayesian methods for model selection in generalized HMMs.

1999 - Machine Learning
An Algorithm that Learns What's in a Name

In this paper, we present IdentiFinderTM, a hidden Markov model that learns to recognize and classify names, dates, times, and numerical quantities. We have evaluated the model in English (based on data from the Sixth and Seventh Message Understanding Conferences [MUC-6, MUC-7] and broadcast news) and in Spanish (based on data distributed through the First Multilingual Entity Task [MET-1]), and on speech input (based on broadcast news). We report results here on standard materials only to quantify performance on data available to the community, namely, MUC-6 and MET-1. Results have been consistently better than reported by any other learning algorithm. IdentiFinder's performance is competitive with approaches based on handcrafted rules on mixed case text and superior on text where case information is not available. We also present a controlled experiment showing the effect of training set size on performance, demonstrating that as little as 100,000 words of training data is adequate to get performance around 90% on newswire. Although we present our understanding of why this algorithm performs so well on this class of problems, we believe that significant improvement in performance may still be possible.

2000
Outliers in multivariate time series

This paper considers outliers in multivariate time series analysis. It generalizes four types of disturbances commonly used in the univariate time series analysis to the multivariate case, and investigates dynamic effects of a multivariate outlier on individual components if marginal models are used. An innovational outlier of a vector series can introduce a patch of outliers for the marginal component models. The paper also proposes an iterative procedure to detect and handle multiple outliers. By comparing and contrasting results of univariate and multivariate outlier detections, one can gain insights into the characteristics of an outlier. An outlier in a component series mayor may not have significant impacts on the other components. We use real examples to demonstrate the proposed analysis.

1994
Hidden Markov Models: Estimation and Control

Hidden Markov Model Processing.- Discrete-Time HMM Estimation.- Discrete States and Discrete Observations.- Continuous-Range Observations.- Continuous-Range States and Observations.- A General Recursive Filter.- Practical Recursive Filters.- Continuous-Time HMM Estimation.- Discrete-Range States and Observations.- Markov Chains in Brownian Motion.- Two-Dimensional HMM Estimation.- Hidden Markov Random Fields.- HMM Optimal Control.- Discrete-Time HMM Control.- Risk-Sensitive Control of HMM.- Continuous-Time HMM Control.

2014
Analyzing Neural Time Series Data: Theory and Practice

This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the "analyze now" button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.

2012 - IEEE Transactions on Audio, Speech, and Language Processing
Acoustic Modeling Using Deep Belief Networks

Gaussian mixture models are currently the dominant technique for modeling the emission distribution of hidden Markov models for speech recognition. We show that better phone recognition on the TIMIT dataset can be achieved by replacing Gaussian mixture models by deep neural networks that contain many layers of features and a very large number of parameters. These networks are first pre-trained as a multi-layer generative model of a window of spectral feature vectors without making use of any discriminative information. Once the generative pre-training has designed the features, we perform discriminative fine-tuning using backpropagation to adjust the features slightly to make them better at predicting a probability distribution over the states of monophone hidden Markov models.

1998 - IEEE Trans. Signal Process.
Wavelet-based statistical signal processing using hidden Markov models

Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. We develop a new framework for statistical signal processing based on wavelet-domain hidden Markov models (HMMs) that concisely models the statistical dependencies and non-Gaussian statistics encountered in real-world signals. Wavelet-domain HMMs are designed with the intrinsic properties of the wavelet transform in mind and provide powerful, yet tractable, probabilistic signal models. Efficient expectation maximization algorithms are developed for fitting the HMMs to observational signal data. The new framework is suitable for a wide range of applications, including signal estimation, detection, classification, prediction, and even synthesis. To demonstrate the utility of wavelet-domain HMMs, we develop novel algorithms for signal denoising, classification, and detection.

2019 - ICANN
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of multivariate time series data for these systems. The rich sensor data can be continuously monitored for intrusion events through anomaly detection. However, conventional threshold-based anomaly detection methods are inadequate due to the dynamic complexities of these systems, while supervised machine learning methods are unable to exploit the large amounts of data due to the lack of labeled data. On the other hand, current unsupervised machine learning approaches have not fully exploited the spatial-temporal correlation and other dependencies amongst the multiple variables (sensors/actuators) in the system for detecting anomalies. In this work, we propose an unsupervised multivariate anomaly detection method based on Generative Adversarial Networks (GANs). Instead of treating each data stream independently, our proposed MAD-GAN framework considers the entire variable set concurrently to capture the latent interactions amongst the variables. We also fully exploit both the generator and discriminator produced by the GAN, using a novel anomaly score called DR-score to detect anomalies by discrimination and reconstruction. We have tested our proposed MAD-GAN using two recent datasets collected from real-world CPS: the Secure Water Treatment (SWaT) and the Water Distribution (WADI) datasets. Our experimental results showed that the proposed MAD-GAN is effective in reporting anomalies caused by various cyber-intrusions compared in these complex real-world systems.

Modeling of multivariate time series using hidden markov models

Padhraic Smyth
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Sergey Kirshner
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Padhraic Smyth
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Sergey Kirshner

Abstract:Vector-valued (or multivariate) time series data commonly occur in various sciences. While modeling univariate time series is well-studied, modeling of multivariate time series, especially finite-valued or categorical, has been relatively unexplored. In this dissertation, we employ hidden Markov models (HMMs) to capture temporal and multivariate dependencies in the multivariate time series data. We modularize the process of building such models by separating the modeling of temporal dependence, multivariate dependence, and non-stationary behavior. We also propose new methods of modeling multivariate dependence for categorical and real-valued data while drawing parallels between these two seemingly different types of data. Since this work is in part motivated by the problem of prediction precipitation over geographic regions from the multiple weather stations, we present in detail models pertinent to this hydrological application and perform a thorough analysis of the models on data collected from a number of different geographic regions.

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引用
A comparison of three multi-site statistical downscaling models for daily rainfall in the North China Plain
Theoretical and Applied Climatology
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Unsupervised learning algorithms for intrusion detection
NOMS 2008 - 2008 IEEE Network Operations and Management Symposium
2008
Stochastic Precipitation Generation for the Potomac River Basin Using Hidden Markov Models
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Stock prediction: an event-driven approach based on bursty keywords
Frontiers of Computer Science in China
2009
Statistical downscaling of daily precipitation over Greece
2008
Towards a Time Series Approach for the Classification and Evaluation of Collaborative Activities
Comput. Informatics
2015
Efficient Bayesian estimation of the multivariate Double Chain Markov Model
Stat. Comput.
2013
Modelling runoff with statistically downscaled daily site, gridded and catchment rainfall series
2013
Removing Object Bouncing from Indoor Tracking Data
ACM Southeast Regional Conference
2017
Learning-Based Cleansing for Indoor RFID Data
SIGMOD Conference
2016
Products of Hidden Markov Models: It Takes N>1 to Tango
UAI
2009
Hierarchical Bayesian Approach to a Multi-Site Hidden Markov Model
Commun. Stat. Simul. Comput.
2014
Network anomaly detection by continuous hidden markov models: An evolutionary programming approach
Intell. Data Anal.
2015
Composable, Distributed-state Models for High-dimensional Time Series
2009
Integrating Multiple Data Sources for Stock Prediction
WISE
2008
Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH
PLoS Comput. Biol.
2007
Bayesian nonhomogeneous Markov models via Pólya-Gamma data augmentation with applications to rainfall modeling
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2017
Subseasonal‐to‐interdecadal variability of the Australian monsoon over North Queensland
2006
Cognitive Load Estimation in the Wild
CHI
2018
Multi-period classification: learning sequent classes from temporal domains
Data Mining and Knowledge Discovery
2014