A Hidden Markov Ensemble Algorithm Design for Time Series Analysis

Lin, Ting‐Hui; Wang, Miao; Yang, Min; Yang, Xu

doi:10.3390/s22082950

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…As undisturbed soil, the air-dried soil was used for indoor hyperspectral measurement. A 1:5 soil-water ratio extraction solution was prepared from each air-dried soil sample to determine the total salt content of the soil [39][40]. Using the Markov distance method [41][42], samples were analyzed, and 96 valid samples were obtained as research samples after eliminating 2 abnormal samples.…”

Section: Figure 1 Geographical Location Of the Study Areamentioning

confidence: 99%

Application of a Bi-Directional Gated Recurrent Unit Combined with a Recurrent Neural Network Model Based on Fusion Attention Mechanism in Estimating Soil Salinity

Zhao,

Wang,

Zhang

2023

Preprint

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Soil salinization is an important limiting factor for agricultural and environmental sustainable development. To achieve rapid and accurate identification of soil salt content, a classification model called Attention-bidirectional gate recurrent unit recurrent neural network (Att-BiGRU-RNN) is designed, incorporating the fusion of attention mechanism. In the encoding and decoding modules of the model, BiGRU and RNN structures are used, enabling the extraction of deep spectral features by leveraging the correlation between spectral information in different bands of hyperspectral data. The attention mechanism is introduced to dynamically allocate weight information based on the differences in spectral information, thereby increasing the contribution of important spectral features to the classification model and improving the accuracy of the model. The research area is initially set in Dinge County, Shaanxi Province, China. Field spectroscopy measurements of 120 samples of original and air-dried soils are conducted using a ground-based spectrometer. Different mixed models for estimating soil salt content, including FDT-SVR, FDT-CNN, BiGRU-RNN, and Att-BiGRU-RNN, are constructed and validated and compared. The results show that compared to other models, the Att-BiGRU-RNN model optimized by the attention mechanism exhibits the highest prediction accuracy, with a coefficient of determination R2 = 0.932 and root mean square error RMSE = 0.012. Additionally, the model's recall curve at different precision levels is obtained to meet the parameter selection requirements under different estimation demands. This method can effectively identify areas with high soil salt content or severe salinization based on portable hyperspectral sensors and unmanned aerial vehicle platforms, and statistically analyze the distribution of soil salt content.

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Section: Figure 1 Geographical Location Of the Study Areamentioning

confidence: 99%

Application of a Bi-Directional Gated Recurrent Unit Combined with a Recurrent Neural Network Model Based on Fusion Attention Mechanism in Estimating Soil Salinity

Zhao,

Wang,

Zhang

2023

Preprint

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“…It is developed on the basis of the Markov chain, which is a discrete memoryless random process responsible for describing the relationship between the sequence of states of the next moment with the current one [83][84][85]. An HMM is an evolution of a Markov chain that requires two stochastic processes, adding a random relationship between the sequence of states and the observation vector, and where the sequence of states cannot be directly observed [83,84,[86][87][88][89]. Then, an HMM is a probabilistic time series model, doubly stochastic, which includes the transition of hidden states and emitting observations [90].…”

Section: Hidden Markov Models (Hmms)mentioning

confidence: 99%

Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

et al. 2023

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The maintenance paradigm has evolved over the last few years and companies that want to remain competitive in the market need to provide condition-based maintenance (CBM). The diagnosis and prognosis of the health status of equipment, predictive maintenance (PdM), are fundamental strategies to perform informed maintenance, increasing the company’s profit. This article aims to present a diagnosis and prognosis methodology using a hidden Markov model (HMM) classifier to recognise the equipment status in real time and a deep neural network (DNN), specifically a gated recurrent unit (GRU), to determine this same status in a future of one week. The data collected by the sensors go through several phases, starting by cleaning them. After that, temporal windows are created in order to generate statistical features of the time domain to better understand the equipment’s behaviour. These features go through a normalisation to produce inputs for a feature extraction process, via a principal component analysis (PCA). After the dimensional reduction and obtaining new features with more information, a clustering is performed by the K-means algorithm, in order to group similar data. These clusters enter the HMM classifier as observable states. After training using the Baum–Welch algorithm, the Viterbi algorithm is used to find the best path of hidden states that represent the diagnosis of the equipment, containing three states: state 1—“State of Good Operation”; state 2—“Warning State”; state 3—“Failure State”. Once the equipment diagnosis is complete, the GRU model is used to predict the future, both of the observable states as well as the hidden states coming out from the HMM. Thus, through this network, it is possible to directly obtain the health states 7 days ahead, without the necessity to run the whole methodology from scratch.

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“…While it can be relevant to measure contemporaneous similarities in time series, the distance is not well suited in case of differences in the time dimension [1]. Dynamic Time Warping (DTW) is an algorithm that efficiently determines the nonlinear alignment of two time series that is optimal with respect to a set of criteria [2][3][4][5]. Thereby, the algorithm is not distorted through differences in the time domain, such as leads or lags.…”

Section: Introductionmentioning

confidence: 99%

Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships

Stübinger

Walter

2022

Sensors

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This paper develops a multi-dimensional Dynamic Time Warping (DTW) algorithm to identify varying lead-lag relationships between two different time series. Specifically, this manuscript contributes to the literature by improving upon the use towards lead-lag estimation. Our two-step procedure computes the multi-dimensional DTW alignment with the aid of shapeDTW and then utilises the output to extract the estimated time-varying lead-lag relationship between the original time series. Next, our extensive simulation study analyses the performance of the algorithm compared to the state-of-the-art methods Thermal Optimal Path (TOP), Symmetric Thermal Optimal Path (TOPS), Rolling Cross-Correlation (RCC), Dynamic Time Warping (DTW), and Derivative Dynamic Time Warping (DDTW). We observe a strong outperformance of the algorithm regarding efficiency, robustness, and feasibility.

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A Hidden Markov Ensemble Algorithm Design for Time Series Analysis

Cited by 3 publications

References 21 publications

Application of a Bi-Directional Gated Recurrent Unit Combined with a Recurrent Neural Network Model Based on Fusion Attention Mechanism in Estimating Soil Salinity

Application of a Bi-Directional Gated Recurrent Unit Combined with a Recurrent Neural Network Model Based on Fusion Attention Mechanism in Estimating Soil Salinity

Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships

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