Lempel-Ziv Complexity Algorithm Research Articles

Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm

Effective carbon pricing policies have become an effective tool for many countries to encourage emission reduction. An accurate carbon price prediction model is helpful for the implementation of energy conservation and emission reduction policies and the decision-making of governments and investors. However, it is difficult for a single prediction model to achieve high prediction accuracy because of the high complexity of the carbon price series. Many studies have proved the nonlinear characteristics of carbon trading prices, but there are very few studies on the chaotic nature of carbon price series. As a consequence, this paper proposes an innovative hybrid model for carbon price prediction. A decomposition-reconstruction-prediction-integration scheme is designed to predict carbon prices. Firstly, several intrinsic mode functions (IMFs) and one residue were obtained from the raw data decomposed by ICEEMDAN. Next, the decomposed subsection is reconstructed into a new sequence according to the calculation results by the Lempel-Ziv complexity algorithm. Then, considering the chaotic characteristics of sequence, the input variables of the models are determined through the phase space reconstruction (PSR) algorithm combined with the partial autocorrelation function (PACF). Finally, the Sparrow search algorithm (SSA) is introduced to optimize the extreme learning machine (ELM) model, which is applied in the carbon price prediction for the purpose of verifying the validity of the proposed combination model, which is applied to the pilots of Hubei, Beijing, and Guangdong. The empirical results show that the combination model outperformed the 13 other models in predicting accuracy, speed, and stability. The decomposition-reconstruction-prediction-integration strategy is a method for predicting the carbon price efficiently.

Using Lempel-Ziv complexity as effective classification tool of the sleep-related breathing disorders

Background and objectivePeople suffer from sleep disorders caused by work-related stress, irregular lifestyle or mental health problems. Therefore, development of effective tools to diagnose sleep disorders is important. Recently, to analyze biomedical signals Information Theory is exploited. We propose efficient classification method of sleep anomalies by applying entropy estimating algorithms to encoded ECGs signals coming from patients suffering from Sleep-Related Breathing Disorders (SRBD). MethodsFirst, ECGs were discretized using the encoding method which captures the biosignals variability. It takes into account oscillations of ECG measurements around signals averages. Next, to estimate entropy of encoded signals Lempel–Ziv complexity algorithm (LZ) which measures patterns generation rate was applied. Then, optimal encoding parameters, which allow distinguishing normal versus abnormal events during sleep with high sensitivity and specificity were determined numerically. Simultaneously, subjects’ states were identified using acoustic signal of breathing recorded in the same period during sleep. ResultsRandom sequences show normalized LZ close to 1 while for more regular sequences it is closer to 0. Our calculations show that SRBDs have normalized LZ around 0.32 (on average), while control group has complexity around 0.85. The results obtained to public database are similar, i.e. LZ for SRBDs around 0.48 and for control group 0.7. These show that signals within the control group are more random whereas for the SRBD group ECGs are more deterministic. This finding remained valid for both signals acquired during the whole duration of experiment, and when shorter time intervals were considered. Proposed classifier provided sleep disorders diagnostics with a sensitivity of 93.75 and specificity of 73.00%. To validate our method we have considered also different variants as a training and as testing sets. In all cases, the optimal encoding parameter, sensitivity and specificity values were similar to our results above. ConclusionsOur pilot study suggests that LZ based algorithm could be used as a clinical tool to classify sleep disorders since the LZ complexities for SRBD positives versus healthy individuals show a significant difference. Moreover, normalized LZ complexity changes are related to the snoring level. This study also indicates that LZ technique is able to detect sleep abnormalities in early disorders stage.

Cortical dynamics during psychedelic and anesthetized states induced by ketamine

Ketamine is a unique drug that has psychedelic and anesthetic properties in a dose-dependent manner. Recent studies have shown that ketamine anesthesia appears to maintain the spatiotemporal complexity of cortical activation evoked by transcranial magnetic stimulation, while a psychedelic dose of ketamine is associated with increased spontaneous magnetoencephalographic signal diversity. However, a systematic investigation of the dose-dependent effects of ketamine on cortical complexity using the same modality is required. Furthermore, it is unknown whether the complexity level stabilizes or fluctuates over time for the duration of ketamine exposure. Here we investigated the spatiotemporal complexity of spontaneous high-density scalp electroencephalography (EEG) signals in healthy volunteers during alterations of consciousness induced by both subanesthetic and anesthetic doses of ketamine. Given the fast transient spectral dynamics, especially during the gamma-burst pattern after loss of consciousness, we employed a method based on Hidden Markov modeling to classify the EEG signals into a discrete set of brain states that correlated with different behavioral states. We characterized the spatiotemporal complexity specific for each brain state as measured through the Lempel-Ziv complexity algorithm. After controlling for signal diversity due to spectral changes, we found that the subanesthetic dose of ketamine is associated with an elevated complexity level relative to baseline, while the brain activity following an anesthetic dose of ketamine is characterized by alternating low and high complexity levels until stabilizing at a high level comparable to that during baseline. Thus, spatiotemporal complexity associated with ketamine-induced state transitions has features of general anesthesia, normal consciousness, and altered states of consciousness. These results improve our understanding of the complex pharmacological, neurophysiological, and phenomenological properties of ketamine.

Fault Diagnosis of Rolling Element Bearing Using Multi-Scale Lempel-Ziv Complexity and Mahalanobis Distance Criterion

A new fault diagnosis technique for rolling element bearing using multi-scale Lempel-Ziv complexity (LZC) and Mahalanobis distance (MD) criterion is proposed in this study. A multi-scale coarse-graining process is used to extract fault features for various bearing fault conditions to overcome the limitation of the single stage coarse-graining process in the LZC algorithm. This is followed by the application of MD criterion to calculate the accuracy rate of LZC at different scales, and the best scale corresponding to the maximum accuracy rate is identified for fault pattern recognition. A comparison analysis with Euclidean distance (ED) criterion is also presented to verify the superiority of the proposed method. The result confirms that the fault diagnosis technique using a multi-scale LZC and MD criterion is more effective in distinguishing various fault conditions of rolling element bearings.

A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting

In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating empirical mode decomposition (EMD) and least square support vector machine (LSSVM) with kernel function prototype is proposed for carbon price forecasting. The EMD algorithm is used to decompose the carbon price into simple intrinsic mode functions (IMFs) and one residue, which are identified as the components of high frequency, low frequency and trend by using the Lempel-Ziv complexity algorithm. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to forecast the high frequency IMFs with ARCH effects. The LSSVM model with kernel function prototype is employed to forecast the high frequency IMFs without ARCH effects, the low frequency and trend components. The forecasting values of all the components are aggregated into the ones of original carbon price by the LSSVM with kernel function prototype-based nonlinear ensemble approach. Furthermore, particle swarm optimization is used for model selections of the LSSVM with kernel function prototype. Taking the popular prediction methods as benchmarks, the empirical analysis demonstrates that the proposed model can achieve higher level and directional predictions and higher robustness. The findings show that the proposed model seems an advanced approach for predicting the high nonstationary, nonlinear and irregular carbon price.

Complexity measurement of regional groundwater resources system using improved Lempel-Ziv complexity algorithm

In this paper, the self-adaptive artificial fish swarm algorithm (SAAFSA) is used to optimize the coarse graining of segment numbers, which are used in the Lempel-Ziv complexity algorithm. This approach improves the Lempel-Ziv complexity (LZC) algorithm of equal probability coarse graining. As a case study, the complexities of monthly series of groundwater depth were analyzed at seven farms in the Hongxinglong Administration. GIS technology was used to create spatial distributions of monthly groundwater depths. A projection pursuit model based on the SAAFSA was established and used for complexity attribution analysis at selected farms with different degrees of complexity. The three selected farms, Hongqiling, 852, and Youyi, each represent a certain degree of complexity. Further analysis shows that precipitation, evaporation, temperature, and human activities are the primary factors that cause complexity variations in local groundwater depth. The results reveal the evolution of the complexity characteristics of local groundwater depth and provide scientific evidence for the need to effectively allocate regional water resources. Additionally, the proposed method can be applied in complexity analyses of other hydrologic features, as well as in research regarding nonlinear time series in economic, engineering, medical, and signal analyses.

A novel encoding Lempel–Ziv complexity algorithm for quantifying the irregularity of physiological time series

Background and objectiveThe Lempel–Ziv (LZ) complexity and its variants have been extensively used to analyze the irregularity of physiological time series. To date, these measures cannot explicitly discern between the irregularity and the chaotic characteristics of physiological time series. Our study compared the performance of an encoding LZ (ELZ) complexity algorithm, a novel variant of the LZ complexity algorithm, with those of the classic LZ (CLZ) and multistate LZ (MLZ) complexity algorithms. Methods and resultsSimulation experiments on Gaussian noise, logistic chaotic, and periodic time series showed that only the ELZ algorithm monotonically declined with the reduction in irregularity in time series, whereas the CLZ and MLZ approaches yielded overlapped values for chaotic time series and time series mixed with Gaussian noise, demonstrating the accuracy of the proposed ELZ algorithm in capturing the irregularity, rather than the complexity, of physiological time series. In addition, the effect of sequence length on the ELZ algorithm was more stable compared with those on CLZ and MLZ, especially when the sequence length was longer than 300. A sensitivity analysis for all three LZ algorithms revealed that both the MLZ and the ELZ algorithms could respond to the change in time sequences, whereas the CLZ approach could not. Cardiac interbeat (RR) interval time series from the MIT-BIH database were also evaluated, and the results showed that the ELZ algorithm could accurately measure the inherent irregularity of the RR interval time series, as indicated by lower LZ values yielded from a congestive heart failure group versus those yielded from a normal sinus rhythm group (p < 0.01).

DetectMITE: A novel approach to detect miniature inverted repeat transposable elements in genomes.

Miniature inverted repeat transposable elements (MITEs) are prevalent in eukaryotic genomes, including plants and animals. Classified as a type of non-autonomous DNA transposable elements, they play important roles in genome organization and evolution. Comprehensive and accurate genome-wide detection of MITEs in various eukaryotic genomes can improve our understanding of their origins, transposition processes, regulatory mechanisms, and biological relevance with regard to gene structures, expression, and regulation. In this paper, we present a new MATLAB-based program called detectMITE that employs a novel numeric calculation algorithm to replace conventional string matching algorithms in MITE detection, adopts the Lempel-Ziv complexity algorithm to filter out MITE candidates with low complexity, and utilizes the powerful clustering program CD-HIT to cluster similar MITEs into MITE families. Using the rice genome as test data, we found that detectMITE can more accurately, comprehensively, and efficiently detect MITEs on a genome-wide scale than other popular MITE detection tools. Through comparison with the potential MITEs annotated in Repbase, the widely used eukaryotic repeat database, detectMITE has been shown to find known and novel MITEs with a complete structure and full-length copies in the genome. detectMITE is an open source tool (https://sourceforge.net/projects/detectmite).

Selection of an efficient feature space for EEG-based mental task discrimination

The aim of this paper is to contribute toward exploring an optimal feature space for discriminating mental tasks. Empirical mode decomposition (EMD) algorithm seems useful for designing such a feature space. The adjustment of nonlinear and non-stationary properties of the EEG signals with this algorithm and the successful application of this approach together biomedical signal processing problems encourage us to examine a variety of statistical and spectral measures within the EMD space as the adapted features. In this sense, as a measure of complexity, the Lempel–Ziv algorithm is utilized within the framework of the EMD algorithm. A modified form of the Lempel–Ziv complexity algorithm is then proposed. The features derived from the modified algorithm outperform the other features individually. By combining the modified Lempel–Ziv features with the other adopted features, in average, 97.78% classification accuracy is achieved for different subjects. It is concluded that the EMD–LZ kernel allows for achieving of better performances in classifying mental tasks than the results obtained with other methods.

2698 – Interhemispheric asymmetry in OCD patients an study using non linear analysis of MEG recordings

Introduction Obsessive compulsive disorder (OCD) is characterized by the presence of obsessions and compulsions that cause emotional distress and affect different aspects of a patient's life which are clinically significant. Experts have developed an OCD model which involves hyperactivity in certain subcortical and cortical regions. Objectives To study the differences in the cerebral activity between control and OCD group using Lempel-Ziv complexity (LZC) algorithm, specially focused in the presence of an interhemispheric asymmetry. Methods We analyzed the magnetoencephalographic recordings of 13 patients diagnosed of OCD (DSM and ICD) and 60 healthy controls, using the LZC algorithm. Statistical analysis, using SSPS 19.0, included Wilcoxon signed rank and correlation Tests. Results LZC values were smaller in the OCD than in the control group. An asymmetry between both lateral regions was found in the OCD sample but not in the controls. We studied the difference between the Pearson correlation coefficients (left and right values) between OCD and controls, which was statistically significant. Conclusions Although we know that in humans normal variation and specialization produce asymmetries of function and structure leading to an asymmetry between brain hemispheres, it seems to be more important in some mental disorders. The presence of an asymmetry between both lateral regions is a conclusion found in previous neurophysiological works and that must be integrated in the neuroanatomical model of the disorder.

Improved Complexity Based on Time-Frequency Analysis in Bearing Quantitative Diagnosis

This paper compares and analyzes the quantitative diagnosis methods based on Lempel-Ziv complexity for bearing fault, using continuous wavelet transform (CWT), Empirical Mode Decomposition (EMD) method, and wavelet packet method for decomposition of vibration signal measured by acceleration sensors, respectively. The kurtosis and entropy indices are also analyzed in order to select the optimum analysis area from the vibration signal. The variation trend of vibration signal Lempel-Ziv complexity of bearing inner race and outer race with the varying fault severity is analyzed and predicted based on the generation mechanism and characteristics of fault vibration signals. Experimental results show that it is suitable for observing the fault growing trend and severity by examining the value based on improved Lempel-Ziv complexity algorithm using continuous wavelet transform (CWT), Empirical Mode Decomposition (EMD) method, and wavelet packet method for signal decomposition, using kurtosis indices for optimal analysis area selection, and using the optimal weight coefficients for complexity calculation.

Discrete Capability of the Lempel–Ziv Complexity Algorithm on a Vibration Sequence

The Lempel–Ziv complexity (LZC) method is used to analyze the acceleration response of the T-shaped plate. The response is converted into symbolic sequences with the multi-segmented coarse-grained method. The LZC of the response of 240 points located in different areas near the center is calculated. The results show that LZC arithmetic applied to elastomer vibration can satisfy the discreteness condition.