Mining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the angle between two time (...) series to define a dissimilarity is analyzed. Moreover, our proposed measure satisfies the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. The experiments show that angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, the accelerated algorithm outperforms the standard one as redundancies are pruned. Our approach has been applied to discover typical patterns of information diffusion in an online social network. Analyses revealed the formation mechanisms of different patterns. (shrink)

McTaggart famously introduced the A- and B-series as rival metaphysical accounts of time. This paper shall reorient the debate over the original distinction. Instead of treating the series as competing theories about the nature of time, it will be argued that they are different viewpoints on a world that is fundamentally physical. To that end, non-reductive physicalism is proposed to reconcile the series.

Time series analysis is a technique to describe the structure and forecast values of a particular variable based on a series of sequential observations. While commonly used in finance and engineering to understand structural changes across time, its applicability to humanistic processes like discourse is less clear. This article demonstrates the feasibility and complementary use of TSA with a case study of metaphor use in psychotherapy sessions. A conceptual sketch of how TSA components relate to discourse components is (...) followed by a step-by-step analysis of two therapist–client dyads. Each dyad is shown to be adequately described by a different model, and the key characteristics of the models are interpreted alongside prior qualitative analysis of the dyads, shedding new light on their structural properties and attendant implications. Limitations of the present analysis and future research directions are briefly discussed. (shrink)

In herein article an attempt of problem solution connected with anomaly detection in network traffic with the use of statistic models with long or short memory dependence was presented. In order to select the proper type of a model, the parameter describing memory on the basis of the Geweke and Porter-Hudak test was estimated. Bearing in mind that the value of statistic model depends directly on quality of data used for its creation, at the initial stage of the suggested method, (...) outliers were identified and then removed. For the implementation of this task, the criterion using the value of interquartile range was used. The data prepared in this manner were useful for automatic creation of statistic models classes, such as ARFIMA and Holt-Winters. The procedure of calculation of model parameters’ optimal values was carried out as a compromise between the models coherence and the size of error estimation. Then, relations between the estimated network model and its actual parameters were used in order to detect anomalies in the network traffic. Considering the possibility of appearance of significant real traffic network fluctuations, procedure of updating statistic models was suggested. The results obtained in the course of performed experiments proved efficacy and efficiency of the presented solution. (shrink)

Standard models in quantitative linguistics assume that word usage follows a fixed frequency distribution, often Zipf's law or a close relative. This view, however, does not capture the near daily variations in topics of conversation, nor the short-term dynamics of language change. In order to understand the dynamics of human language use, we present a corpus of daily word frequency variation scraped from online news sources every 20 min for more than 2 years. We construct a simple time-varying model with (...) a latent state, which is observed via word frequency counts. We use Bayesian techniques to infer the parameters of this model for 20,000 words, allowing us to convert complex word-frequency trajectories into low-dimensional parameters in word usage. By analyzing the inferred parameters of this model, we quantify the relative mobility and drift of words on a day-to-day basis, while accounting for sampling error. We quantify this variation and show evidence against “rich-get-richer” models of word use, which have been previously hypothesized to explain statistical patterns in language. (shrink)

Feeling moved or touched can be accompanied by tears, goosebumps, and sensations of warmth in the centre of the chest. The experience has been described frequently, but psychological science knows little about it. We propose that labelling one’s feeling as being moved or touched is a component of a social-relational emotion that we term kama muta. We hypothesise that it is caused by appraising an intensification of communal sharing relations. Here, we test this by investigating people’s moment-to-moment reports of feeling (...) moved and touched while watching six short videos. We compare these to six other sets of participants’ moment-to-moment responses watching the same videos: respectively, judgements of closeness, reports of weeping, goosebumps, warmth in the centre of the chest, happiness, and sadness. Our eighth time series is expert ratings of communal sharing. Time series analyses show strong and consistent cross-correlations of feeling moved and touched and closeness with each other and with each of the three physiological variables and expert-rated communal sharing – but distinctiveness from happiness and sadness. These results support our model. (shrink)

The temporal structure of behavior contains a rich source of information about its dynamic organization, origins, and development. Today, advances in sensing and data storage allow researchers to collect multiple dimensions of behavioral data at a fine temporal scale both in and out of the laboratory, leading to the curation of massive multimodal corpora of behavior. However, along with these new opportunities come new challenges. Theories are often underspecified as to the exact nature of these unfolding interactions, and psychologists have (...) limited ready-to-use methods and training for quantifying structures and patterns in behavioral time series. In this paper, we will introduce four techniques to interpret and analyze high-density multi-modal behavior data, namely, to: (1) visualize the raw time series, (2) describe the overall distributional structure of temporal events (Burstiness calculation), (3) characterize the nonlinear dynamics over multiple timescales with Chromatic and Anisotropic Cross-Recurrence Quantification Analysis (CRQA), (4) and quantify the directional relations among a set of interdependent multimodal behavioral variables with Granger Causality. Each technique is introduced in a module with conceptual background, sample data drawn from empirical studies and ready-to-use Matlab scripts. The code modules showcase each technique’s application with detailed documentation to allow more advanced users to adapt them to their own datasets. Additionally, to make our modules more accessible to beginner programmers, we provide a “Programming Basics” module that introduces common functions for working with behavioral timeseries data in Matlab. Together, the materials provide a practical introduction to a range of analyses that psychologists can use to discover temporal structure in high-density behavioral data. (shrink)

Elliot Sober ([2001]) forcefully restates his well-known counterexample to Reichenbach's principle of the common cause: bread prices in Britain and sea levels in Venice both rise over time and are, therefore, correlated; yet they are ex hypothesi not causally connected, which violates the principle of the common cause. The counterexample employs nonstationary data—i.e., data with time-dependent population moments. Common measures of statistical association do not generally reflect probabilistic dependence among nonstationary data. I demonstrate the inadequacy of the counterexample and of (...) some previous responses to it, as well as illustrating more appropriate measures of probabilistic dependence in the nonstationary case. A challenge to the principle of the common causeSober's argument and the attempts to rescue the principleProbabilistic dependenceNonstationary time seriesProbabilistic dependence in nonstationary time seriesDo Venetian sea levels and British bread prices violate the principle of the common cause? (shrink)

Electricity is an indicator that shows the progress of a civilization; it is a product that has greatly changed the way we think about the world. Electricity price forecasting became a fundamental task in all countries due to the deregulation of the electricity market in the 1990s. This work examines the effectiveness of using multiple variables for price prediction given the large number of factors that could influence the price of the electricity market. The tests were carried out over four (...) periods using data from Spain and deep learning models. Two different attribute selection methods based on Pearson’s correlation coefficient have been used to improve the efficiency of the training process. The variables used as input to the different prediction models were chosen, considering those most commonly used previously in the literature. This study attempts to test whether using time series lags improves the non-use of lags. The results obtained have shown that lags improve the results compared to a previous work in which no lags were used. (shrink)

This work presents a novel approach to forecast streaming big time series based on nearest similar patterns. This approach combines a clustering algorithm with a classifier and the nearest neighbours algorithm. It presents two separate stages: offline and online. The offline phase is for training and finding the best models for clustering, classification and the nearest neighbours algorithm. The online phase is to predict big time series in real time. In the offline phase, data are divided into clusters (...) and a forecasting model based on the nearest neighbours is trained for each cluster. In addition, a classifier is trained using the cluster assignments previously generated by the clustering algorithm. In the online phase, the classifier predicts the cluster label of an instance, and the proper nearest neighbours model according to the predicted cluster label is applied to obtain the final prediction using the similar patterns. The algorithm is able to be updated incrementally for online learning from data streams. Results are reported using electricity consumption with a granularity of |$10$| minutes for 4-hour-ahead forecasting and compared with well-known online benchmark learners, showing a remarkable improvement in prediction accuracy. (shrink)

In the industrial context, steel is a broadly-used raw material with applications in many different fields. Due to its high impact in the activity of many industries all over the world, forecasting its price is of utmost importance for a huge amount of companies. In this work, non-linear neural models are applied for the first time to different datasets in order to validate their suitability when predicting the price of this commodity. In particular, the NAR, NIO and NARX neural network (...) models are innovatively applied for the first time to forecast the price of hot rolled steel in Spain. Besides these variety of models, different datasets consisting of a set of heterogenous variables from the last seven years and related to the price of this commodity are benchmarked and analyzed. The results showed that NARX is the best performing model when the price of raw materials used to produce steel and the stock market prices of three major global steel producing companies are employed as input to this predictive model. Consequently, this result may boost the application of Machine Learning in companies, in order to schedule the supplying operations according to the price forecasting. (shrink)

In the absence of continuing selves or persons, Buddhist philosophers are under pressure to provide a systematic account of phenomenological and other features of conscious experience. Any such Buddhist account of experience, however, faces further problems because of another cardinal tenet of Buddhist revisionary metaphysics: the doctrine of impermanence, which during the Abhidharma period is transformed into the doctrine of momentariness. Setting aside the problems that plague the Buddhist Abhidharma theory of experience because of lack of persons, I shall focus (...) on problems that arise because of its allegiance to momentariness and explore some responses on behalf of the Abhidharma Buddhist philosophers. I address two challenges to the Buddhist view in this paper. The first, which I will call the “Phenomenological Challenge”, primarily concerns the temporal properties of what is represented in conscious experience. The second, which I will call the “Metaphysical Challenge”, concerns the temporal properties of conscious representation itself. (shrink)

Nowadays decision making is strongly supported by the high-confident point estimations produced by deep learning algorithms. In many activities, they are sufficient for the decision-making process. However, in some other cases, confidence intervals are required too for an appropriate decision-making process. In this work, a first attempt to generate point estimations with confidence intervals for the $^{222}$Rn radiation level time series at Canfranc Underground Laboratory is presented. To predict the low-radiation periods allows correctly scheduling the unshielded periods for maintenance (...) operations in the experiments hosted in this facility. This should minimize the deposition of radioactive dust on the exposed surfaces during these unshielded periods. An approach based on deep learning with stochastic regulation is evaluated in the forecasting of point estimations and confidence intervals of the $^{222}$Rn time series and compared with a second approach based on Gaussian processes. As a consequence of this work, an evaluation of the capacity of Gaussian process and deep learning with stochastic regularization for generating point estimations and their confidence intervals for this time series is stated. (shrink)

Emotional experiences typically labelled “being moved” or “feeling touched” may belong to one universal emotion. This emotion, which has been labelled “kama muta”, is hypothesised to have a positive valence, be elicited by sudden intensifications of social closeness, and be accompanied by warmth, goosebumps and tears. Initial evidence on correlations among the kama muta components has been collected with self-reports after or during the emotion. Continuous measures during the emotion seem particularly informative, but previous work allows only restricted inferences on (...) intra-individual processes because time series were cross-correlated across samples. In the current studies, we instead use a within-subject design to replicate and extend prior work. We compute intra-individual cross-correlations between continuous self-reports on feeling moved and (1) positive and negative affect; (2) goosebumps and subjective warmth and (3) appraisals of closeness and morality. Results confirm the predictions of kama muta theory that feeling moved by intensified communal sharing cross-correlates with appraised closeness, positive affect, warmth and (less so) goosebumps, but not with negative affect. Contrary to predictions, appraised morality cross-correlated with feeling moved as much as appraised closeness did. We conclude that strong inferences on emotional processes are possible using continuous measures, replace earlier findings, and are largely in line with theorising. (shrink)

The present study aims at proposing a time series-based network public opinion emergency management decision support algorithm for the problems of low decision accuracy and long decision time in traditional similar algorithms. In this proposed algorithm, after the time series data are preprocessed, the association rules of the original indicator data of network public opinion emergencies are mined, the original indicator data matrix of NPOEs will be constructed, and the improved local linear embedding approach will be employed to (...) obtain the original indicator data of NPOEs. After carrying out the preprocessing according to the preprocessing results, the objective weight of the emergency decision index is calculated through the interval value fuzzy information entropy measurement of the emergency decision index, the emergency management decision support model is constructed, and the emergency management decision support of the NPOE is realized. The simulation experiment results show that the proposed algorithm has a better effect on the decision-making effect of the management of NPOEs, and a higher decision-making efficiency is achieved. (shrink)

This study aimed to identify real-life experts working for a port authority and lay people (students) who played The Sustainable Port, a serious game aiming to simulate the dynamics occurring in a port area. To achieve this goal, we analyzed eye gaze data collected noninvasively using low-grade webcams from 28 participants working for the port authority of the Port of Rotterdam and 66 students. Such data were used for a classification task implemented using a MiniRocket classifier, an algorithm used for (...) time-series classification. The classifier reached an F1 score of 0.75 (SD = 0.07), a PR AUC of 0.73 (SD = 0.14), and an ROC AUC of 0.75 (SD = 0.15) providing evidence that it is possible to identify real-life experts about maritime port management using data that can be obtained from a webcam. We speculate that the gaze direction used to train the MiniRocket may contain relevant information about the cognitive processes and decisions occurring throughout the gameplay. We suggest that the methods here presented not only can be used to detect experts playing simulations, such as serious games, but also to identify experts tackling screen-presented tasks. (shrink)

Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels from simpler to (...) more complex ones. Simpler levels handle smoothed versions of the input, whereas the most complex level processes the original time series. This method follows the human learning process where general/simpler tasks are performed first, and afterward, more precise/harder ones are accomplished. Our proposed methodology has been applied to the LSTM architecture, showing remarkable performance in various time series. In addition, a comparison is reported including a standard LSTM and novel methods such as DeepAR, Temporal Fusion Transformer, NBEATS and Echo State Network. (shrink)

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This (...) task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity. (shrink)

In this paper, a size-independent modification of the general detrended fluctuation analysis method is introduced. With this modified DFA, seismic time series pertaining to most seismically active regions of the world from the year1972up to the year2016are comparatively analyzed. An eminent homogeneity of spatial clustering behaviors in worldwide range is detected and DFA scaling exponents coincide with previous results for local regions. Furthermore, universal nontrivial spatial clustering behaviors are revealed from shallow to intermediate-depth earthquakes by varying the depth of (...) the hypocenter: in shallow earthquakes, the depth range corresponding to the minimum spatial clustering is first verified to be spatiotemporal and identical; in intermediate-depth earthquakes, strong weakening long-range correlations compared to shallow earthquakes are unveiled to strengthen the recent findings in seismology. Our work aims to suggest a possible statistical approach to explore the dynamic mechanisms implied in the seismicity as well as in other analogous dynamic evolution processes. (shrink)

Arguments about the nature of teaching have depended principally on naturalistic observation and some experimental work. Additional measurement tools, and physiological variations and manipulations can provide insights on the intrinsic structure and state of the participants better than verbal descriptions alone: namely, time-series analysis, and examination of the role of hormones and neuromodulators on the behaviors of teacher and pupil.

This thesis describes a method to control rare events in non-equilibrium systems by applying physical forces to those systems but without relying on numerical simulation techniques, such as copying rare events. In order to study this method, the book draws on the mathematical structure of equilibrium statistical mechanics, which connects large deviation functions with experimentally measureable thermodynamic functions. Referring to this specific structure as the "phenomenological structure for the large deviation principle", the author subsequently extends it to time-series statistics (...) that can be used to describe non-equilibrium physics. The book features pedagogical explanations and also shows many open problems to which the proposed method can be applied only to a limited extent. Beyond highlighting these challenging problems as a point of departure, it especially offers an effective means of description for rare events, which could become the next paradigm of non-equilibrium statistical mechanics. (shrink)

In order to reduce the dimensionality of parameter space and enhance out-of-sample forecasting performance, this research compares regularization techniques with Autometrics in time-series modeling. We mainly focus on comparing weighted lag adaptive LASSO with Autometrics, but as a benchmark, we estimate other popular regularization methods LASSO, AdaLASSO, SCAD, and MCP. For analytical comparison, we implement Monte Carlo simulation and assess the performance of these techniques in terms of out-of-sample Root Mean Square Error, Gauge, and Potency. The comparison is assessed (...) with varying autocorrelation coefficients and sample sizes. The simulation experiment indicates that, compared to Autometrics and other regularization approaches, the WLAdaLASSO outperforms the others in covariate selection and forecasting, especially when there is a greater linear dependency between predictors. In contrast, the computational efficiency of Autometrics decreases with a strong linear dependency between predictors. However, under the large sample and weak linear dependency between predictors, the Autometrics potency ⟶ 1 and gauge ⟶ α. In contrast, LASSO, AdaLASSO, SCAD, and MCP select more covariates and possess higher RMSE than Autometrics and WLAdaLASSO. To compare the considered techniques, we made the Generalized Unidentified Model for covariate selection and out-of-sample forecasting for the trade balance of Pakistan. We train the model on 1985–2015 observations and 2016–2020 observations as test data for the out-of-sample forecast. (shrink)

Flooding is one of the most damaging natural hazards globally. During the past three years, floods have claimed hundreds of lives and millions of dollars of damage in Iran. In this study, we detected flood locations and mapped areas susceptible to floods using time series satellite data analysis as well as a new model of bagging ensemble-based alternating decision trees, namely, bag-ADTree. We used Sentinel-1 data for flood detection and time series analysis. We employed twelve conditioning parameters of (...) elevation, normalized difference’s vegetation index, slope, topographic wetness index, aspect, curvature, stream power index, lithology, drainage density, proximities to river, soil type, and rainfall for mapping areas susceptible to floods. ADTree and bag-ADTree models were used for flood susceptibility mapping. We used software of Sentinel application platform, Waikato Environment for Knowledge Analysis, ArcGIS, and Statistical Package for the Social Sciences for preprocessing, processing, and postprocessing of the data. We extracted 199 locations as flooded areas, which were tested using a global positioning system to ensure that flooded areas were detected correctly. Root mean square error, accuracy, and the area under the ROC curve were used to validate the models. Findings showed that root mean square error was 0.31 and 0.3 for ADTree and bag-ADTree techniques, respectively. More findings illustrated that accuracy was obtained as 86.61 for bag-ADTree model, while it was 85.44 for ADTree method. Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. This study can be a good source of information for crisis management in the study area. (shrink)

An emerging perspective on human cognition and performance sees it as a kind of self-organizing phenomenon involving dynamic coordination across the body, brain and environment. Measuring this coordination faces a major challenge. Time series obtained from such cognitive, behavioral, and physiological coordination are often complicated in terms of non-stationarity and non-linearity, and in terms of continuous vs. categorical scales. Researchers have proposed several analytical tools and frameworks. One method designed to overcome these complexities is recurrence quantification analysis, developed in (...) the study of non-linear dynamics. It has been applied in various domains, including linguistic data or motion data. However, most previous studies have applied recurrence methods individually to categorical or continuous data. To understand how complex coordination works, an integration of these types of behavior is needed. We aimed to integrate these methods to investigate the relationship between language and motion directly. To do so, we added temporal information to categorical data, and applied joint recurrence analysis methods to visualize and quantify speech-motion coordination coupling during a rap performance. We illustrate how new dynamic methods may capture this coordination in a small case-study design on this expert rap performance. We describe a case study suggesting this kind of dynamic analysis holds promise, and end by discussing the theoretical implications of studying complex performances of this kind as a dynamic, coordinated phenomenon. (shrink)

COVID-19 is unique in that it is the first global pandemic occurring amidst a crowded information environment that has facilitated the proliferation of misinformation on social media. Dangerous misleading narratives have the potential to disrupt ‘official’ information sharing at major government announcements. Using an interrupted time-series design, we test the impact of the announcement of the first UK lockdown on short-term trends of misinformation on Twitter. We utilise a novel dataset of all COVID-19-related social media posts on Twitter from (...) the UK 48 hours before and 48 hours after the announcement. We find that while the number of tweets increased immediately post announcement, there was no evidence of an increase in misinformation-related tweets. We found an increase in COVID-19-related bot activity post-announcement. Topic modelling of misinformation tweets revealed four distinct clusters: ‘government and policy’, ‘symptoms’, ‘pushing back against misinformation’ and ‘cures and treatments’. (shrink)

There is a number of studies, in which it is established that the observed flows of microposts generated by microblogging social networks are characterized by avalanche-like behavior. Time series of microposts depicting such streams are the time series with a power-law distribution, with 1/f noise and long memory. Despite this, there are no studies devoted to the detection and analysis of self-organized critical state, subcritical phase, and supercritical phase. The presented paper is devoted to the detection and investigation (...) of such critical states and phases. An algorithm is proposed that allowed to detect of critical phases and critical conditions on Twitter, based on the analysis of retweets time series corresponding to the three debates of the 2016 United States Presidential Election, as the most popular debate in the history of America, collecting 84 million live views. (shrink)

With the rapid growth of online product reviews, many users refer to others’ opinions before deciding to purchase any product. However, unfortunately, this fact has promoted the constant use of fake reviews, resulting in many wrong purchase decisions. The effective identification of deceptive reviews becomes a crucial yet challenging task in this research field. The existing supervised learning methods require a large number of labeled examples of deceptive and truthful opinions by domain experts, while the available unsupervised learning methods are (...) inefficient because they depend on the features of reviewers to detect each fake review. Therefore, by focusing on the detection efficiency problem and the limitation of large amount of labeled examples dependence, in this paper, we proposed an effective semisupervised learning approach for detecting spam reviews. Firstly, a time series model of all the reviews of a product is constructed, and then the suspected time intervals are captured based on the burst review increases in these intervals. Secondly, a co-training two-view semisupervised learning algorithm was performed in each captured interval, in which linguistic cues, metadata, and user purchase behaviors were synthetically employed to classify the reviews and check whether they are spam ones or not. A series of numerical experiments on a real dataset acquired from Taobao.com have confirmed the effectiveness of the proposed model, not only reaping benefits in terms of time efficiency and high accuracy but also overcoming the shortcomings of supervised learning methods, which depend on large amounts of labeled examples. And a trade-off balance was obtained between accuracy and efficiency. (shrink)

Cybersecurity aggregates are numerical data obtained by aggregation on features along a database of cybersecurity reports. These aggregates are obtained by integration of time-stamped tables using some recent results of non-standard calculus. Time-series of aggregates are shown to contain relevant information about the concrete system dealt with. Trend time series is also forecasted using known data-driven methods. Although absolute forecasting of trend time series is not obtained, a directional forecasting of trend time series is achieved thence (...) validated by means of a rolling cross validation scheme on a public database of Scareware reports. (shrink)

The irreversibility in time, the multicausality on lines, and the uncertainty of feedbacks make economic systems and the predictions of economic chaotic time series possess the characteristics of high dimensionalities, multiconstraints, and complex nonlinearities. Based on genetic algorithm and fuzzy rules, the chaotic genetics combined with fuzzy decision-making can use simple, fast, and flexible means to complete the goals of automation and intelligence that are difficult to traditional predicting algorithms. Moreover, the new combined method’s ergodicity can perform nonrepetitive searches (...) in a global scope, hence improving the algorithm’s accuracy and efficiency. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of the prediction of economic chaotic time series, elaborated the development background, current status, and future challenges of the combined algorithm of chaotic genetics with fuzzy decision, introduced the basic principles of chaotic genetic algorithm and fuzzy decision algorithm, constructed a prediction model for economic chaotic time series, performed parameter synchronization optimization and moderate function construction, analyzed the prediction processes of economic chaotic time series, conducted phase space reconstruction and correlation dimension calculation, and finally carried out a simulation experiment with its result analysis. The study results show that the algorithm of chaotic genetics combined with fuzzy decision-making can dynamically adjust chaotic mutation operators and summarize fussy expert experiences. The phase space of its reconstructed chaotic attractor has high-precision predictability and can find orderly processes from changeable economic results, which in turn can be used to analyze and predict the complex economic chaotic time series. The study results of this paper provide a reference for further research on predictive analysis of economic chaotic time series based on chaotic genetics combined with fuzzy decision algorithm. (shrink)

Emotion recognition systems have been of interest to researchers for a long time. Improvement of brain-computer interface systems currently makes EEG-based emotion recognition more attractive. These systems try to develop strategies that are capable of recognizing emotions automatically. There are many approaches due to different features extractions methods for analyzing the EEG signals. Still, Since the brain is supposed to be a nonlinear dynamic system, it seems a nonlinear dynamic analysis tool may yield more convenient results. A novel approach in (...) Symbolic Time Series Analysis for signal phase space partitioning and symbol sequence generating is introduced in this study. Symbolic sequences have been produced by means of spherical partitioning of phase space; then, they have been compared and classified based on the maximum value of a similarity index. Obtaining the automatic independent emotion recognition EEG-based system has always been discussed because of the subject-dependent content of emotion. Here we introduce a subject-independent protocol to solve the generalization problem. To prove our method’s effectiveness, we used the DEAP dataset, and we reached an accuracy of 98.44% for classifying happiness from sadness. It was 93.75% for three, 89.06% for four, and 85% for five emotional groups. According to these results, it is evident that our subject-independent method is more accurate rather than many other methods in different studies. In addition, a subject-independent method has been proposed in this study, which is not considered in most of the studies in this field. (shrink)

In order to analyze the characteristics of airport flight delayed time series, based on the construction of flight delay time series, firstly, the K-means algorithm is used to cluster the time series of delayed departures. Secondly, combining with R/s analysis method of Fractal theory, Hurst index of the series is calculated, and Fractal characteristics of the series are analyzed. Then, the VAR (Vector Auto Regression) model is constructed, and Impulse Response Function (IRF) and Variance Decomposition (...) are conducted to explore the impact of the fluctuation of flight delay time series on the future delay. The results show that K-means algorithm divides the time series into five categories, and each category has significant characteristics. Hurst index values of different time series are in the interval of (0.5, 1), indicating that the time series have good fractal characteristics. Through the IRF and Variance Decomposition of VAR model, results show that the time series are significantly affected by random pulses, and the prediction changes of the series come from multiple time series fluctuations. The prediction results show that the flight delay time series is predictable. (shrink)

This paper examines the accounts of limit decision advanced by Hervaeus Natalis and Durand of St. Pourçain in their respective discussions of the sanctification of the Blessed Virgin. Hervaeus and Durand argue, against Aristotle, that the temporal limits of certain changes, including Mary’s sanctification, should be assigned in discrete rather than continuous time. The paper first considers Hervaeus’ discussion of limit decision and argues that, for Hervaeus, a solution of temporal limits in terms of discrete time can coexist with an (...) Aristotelian continuous time solution because Hervaeus takes continuous and discrete time to be two non-intersecting, but correlated time-series. The paper next examines Durand’s account of limit decision and argues that Durand rejects Hervaeus’ correlation assumption as well as Aristotle’s continuous time solution. (shrink)