What is Bootstrapping ? Estimation Confidence Sets and Hypothesis Testing Regression Analysis Forecasting and Time Series Analysis Which Resampling Method Should You Use ? In this paper we present the different approaches to modelling dynamic regression and structural equations• First , we give a survey of the traditional econometrie and the time series approaches to specification analysis of dynamic econometrie models . Then , we describe the three testing procedures that are presently available to the econometrician : ( 1 ) testing for misspecification or diagnostic checking , ( 2 " ) specification analysis or interpretive search , and ( 3 ) checking the overall consistency of the model . However , some of the techniques used remains the same as that used in conventional data mining . As in the case of traditional data base systems , in time series data systems too the methods employed in capturing , indexing , representing and storing the data remains as the key issue . In time series data mining the indexing problem become very critical under the noisy conditions . As wind variability is stochastic , Gaussian Process regression has recently been introduced to capture the randomness of wind energy . However , the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems . A variant Gaussian Process for time series forecasting is introduced in this study to address these issues . The primary goal of this study was to propose an algorithm using mathematical programming to detect earnings management practices . In order to evaluate the ability of this proposed algorithm , the traditional statistical models are used as a benchmark vis-a-vis their time series counterparts . As emerging techniques in the area of mathematical programming yield better results , application of suitable models is expected to result in highly performed forecasts . This paper improves Judicature De-noising Algorithm(JDNA ) in intelligent mechanical ventilation bed system . The judicature process is induced into the de-noising disposal of time series in algorithm , which builds a new de-nosing algorithm . On this basis , this paper optimizes key parameters that affect de-noising effects : illegal factor , the number of data used to formulate noise regulation , the first i legislative data weight , the number of largest evidence data . On the basis of this idea , we proposed a new inference method that relies more on static gene expression data than time-series ones . Through the numerical experiments , we showed that the quality of the inferred genetic network is slightly improved by giving greater importance to static data than time-series ones . Although we develop the new method by modifying the random-forest-based inference method proposed by the authors , we could introduce the idea in this study into any inference method that is capable of analyzing both time-series and static gene expression data . The problem of selecting the most appropriate , based on each specific query , distance measure out of a pool of measures for classification in time series data is also investigated . In addition , searching time series databases via examples , which may be either time series or models , is also part of this work . Moreover , motivated by the nature of music notes in the noisy QBH application , we explore the problem of interval-based sequence matching in a variety of application domains . These universal properties make it possible to estimate how far a system is from a critical threshold . Several early-warning signals have been reported in time series representing systems near catastrophic shifts . The proper understanding of these early-warnings may allow the prediction and perhaps control of these dramatic shifts in a wide variety of systems . In this paper , we present the approaches to identify the malicious users according to information integrity , behavior characteristics and conversation content in early time . Considering statistical methods , we propose the Time Series ( TIME ) model based on the analysis of users ' time behavior characteristics . And thinking from the theme of the users ' session , the improved Latent Dirichlet Allocation ( LDA ) malicious topic model is designed which has improved the sensitivity of the model to detect malicious topics . Additional design considerations relate to compatibility with existing query languages , e.g. , the notion of temporal upward compatibility . However , as illustrated above , an additional hurdle stems from the fact that many ( early ) temporal query languages allowed the users to manipulate a finite underlying representation of temporal databases rather than the actual temporal values/objects in the associated temporal data model . A typical example of this situation would be an approach in which the temporal data model is based on time instants , while the query language introduces interval-valued attributes . Our experiments entailed measuring the hand gestures of subjects for learning purposes and we used these data to create four-layered ANNs , which enabled the proposed system to successfully predict hand gestures in real time . A comparison of the proposed method with other algorithms using temporal data analysis suggested that the hand gesture prediction system using ANNs would be able to forecast various types of hand gestures using resistance data obtained from wearable devices based on PGSs . We investigate issues pertaining to query processing of temporal databases in a relational environment . Tuple-versioning of relations is the adopted method of temporal data representation . New operators are necessary in order to exploit the richer semantics of temporal queries . Secondly , we solved out the key technique of building temporal data topology , successfully developed a suit of spatio-temporal database management system adopting object-oriented methods . The system provides the temporal data collection , data storage , data management and data display and query functions . Finally , as a case study , we explored the application of spatio-temporal data management system with the administrative region data of multi-history periods of China as the basic data . Different collection and processing tools , as well as several datasets of social media data have been developed and published as open-source software . The thesis posits that temporal analysis of data , from social media in particular , provides us with insights into real-world dynamics . Incorporating this temporal information into other applications can benefit standard tasks in natural language processing and beyond . Frequent set mining ( FSM ) , an important phase of association rule mining , is the process of generating frequent sets that satisfy a specified minimum support threshold . This paper explores FSM in temporal data domain or FTP mining and proposes an efficient algorithm for the same . Existing algorithms for FTP mining are based on a priori 's level wise principle . Integration of temporal concepts requires format definitions for temporal values as well as temporal predicates in the query language . The temporal data format is implemented using the GML temporal data type , i.e. , the format to define temporal data types for temporal attributes in the Nexus context model is based on the ISO 8601 standard for temporal values . Using temporal data , the identified main query aspect is concurrency , meaning any overlapping of two temporal values . In order to reduce storage space and allow on-line query , how to trade off data compression effectiveness for on-line query performance is a challenge issue . In this paper , we are concerned with an effective framework for temporal data set that does not scarify on-line query performance and is specifically designed for very large sensor network database . The sampled data are compressed using several candidate approaches including dictionary-base compress and lossless vector quantization . The process of extraction of unknown and new data from a huge dataset which is at times left during classification due to some undefined classification rules is known as novelty detection . We have used various level set methods in order to identify novelty data for temporal data time series . Novelty detection generally focused on the identification of shapes or different patterns , distance and learning based models , density based models , etc . Inversely , these edges prevent grouping of multiple distant hypothesis of the same type , prevent merging two heads of different people . subsection : Temporal Model Regardless of the type of within frame model ( BU or TD / BU ) we rely on the same type of temporal edges that connect nodes of the same type in adjacent frames . In , each refinement step is only indirectly influenced by previous iterations through the accumulation of error feedback . We claim that these refinement iterations should have a more direct and shared temporal representation . To remedy this , we introduce recurrent connections between each iteration ; specifically a long short term memory ( LSTM ) module . Dynamic Bayesian Networks ( DBNs ) are important and widely used to model these sequential data , revealing inherent correlations between data of the systems . Under DBNs , Bayesian inference provides an optimal framework for sequential data analysis and produces solutions potentially better than other non Bayesian-based methods . Yet , intricate computations required by Bayesian inference are barrier to its application . Their algorithm can be applied to any streaming data without requiring a training phase or a priori information , hence processing data on-the-fly and then discarding it . Therefore , the introduced algorithm is especially suitable for the applications requiring sequential data processing at large scales/high rates . The authors present a comprehensive experimental study in stationary and nonstationary environments . These algorithms were simple and easily parallelizable in nature , which enabled them to achieve real-time performance . However , complex enhancement algorithms also require the sequential processing of data and this can not be easily achieved in real-time on a GPU . In this paper , the current advances in mobile CPU and GPU hardware are used to implement video enhancement algorithms in a new way on a mobile computer . Anonymity is provided by utilizing only elements of the model that guarantee differential privacy . Furthermore , we develop a solution for generating differentially private sequential data , which will bring us one step closer to publicly available medical datasets via sequential data . We applied this method to two real medical events datasets and received some encouraging results , demonstrating that the proposed method can be used to publish high quality anonymized data . In many practical settings one can sequentially and adaptively guide the collection of future data , based on information extracted from data collected previously . These sequential data collection procedures are known by different names , such as sequential experimental design , active learning , or adaptive sensing/sampling . The intricate relation between data analysis and acquisition in adaptive sensing paradigms can be extremely powerful , and often allows for reliable signal estimation and detection in situations where nonadaptive sensing would fail dramatically . This paper gives a review of the most important methods for blood velocity vector flow imaging ( VFI ) for conventional sequential data acquisition . This includes multibeam methods , speckle tracking , transverse oscillation , color flow mapping derived VFI , directional beamforming , and variants of these . The present study examines the relationship between the verbal communication skills and paraverbal communication ( both kinesic and proxemic ) of indoor soccer coaches in competitive match situations , senior women 's competitive level . T-patterns detection analysis provides a sequential analysis of data , illustrating the communicative style and flow of each coach . Recurrent Neural Networks ( RNNs ) capture long dependencies and context , and 2 hence are the key component of typical sequential data based tasks . However , the sequential nature of RNNs dictates a large inference cost for long sequences even if the hardware supports parallelization . In this chapter , we consider the hyperspectral classification problems in consideration of sequential data collection , which is a frequent setting in industrial pushboom imaging systems . We present related techniques including data normalization , dimension reduction , classification , and spatial information integration and the way to accommodate these techniques to the context of sequential data collecting and processing . The propose scheme is validated with real data collected in our laboratory . Sequential LSTM models seem to learn syntactic structure from the natural language however their generalization on unseen text is relatively poor comparing with models that exploit syntactic tree structure . Unlike sequential models , recursive neural networks compose word phrases over syntactic tree structure and have shown improved performance in sentiment analysis . However its dependence on a syntactic tree architecture limits practical NLP applications . subsection : Recurrent neural networks ( RNN ) Different from Artificial neural network ( ANN ) , RNN , a neural network with recurrent unit , has a better performance in solving many challenging problems involving sequential data analysis . The state of each time step of the recurrent unit is not only related to the input of the current step , but also related to the state of the previous step . This paper presents a recurrent self-organizing map ( RSOM ) for temporal sequence processing . The RSOM uses the history of a pattern ( i.e. , the previous elements in the sequence ) to compute the best matching unit and to adapt the weights of the map . Temporal sequence analysis is a pracrical technique popular in economic studies both at home and abroad . Based on the theories of econometrics and statistics , this article puts the data into the analysis frame for a purpose of coal price predication in the Chinese markets . As a result of learning , a network is able to associate a pair of events with a temporal delay in a dynamic setting . The results demonstrate that the algorithm can also learn temporal sequence detection . Learning has also been tested in face-voice association using real biometric data . For example performing a series of motor movements or recalling a sequence of episodic memories . Proposed networks which perform temporal sequence generation are often in the form of a modification to an auto-associative memory by using heteroassociative or time-varying synaptic strengths , requiring some pre-chosen temporal functions . Intra-modular synapses are trained auto-associatively with a Hebb rule , while a set of inter-module synapses are hetero-associative . Furthermore , recently proposed dual-event alignment techniques have not been empirically evaluated . In this work , we designed tasks around temporal event sequence and timing analysis and conducted a controlled experiment on Amazon Mechanical Turk to examine four sentinel event alignment approaches : no sentinel event alignment ( NoAlign ) , single-event alignment ( SingleAlign ) , dual-event alignment with left justification ( DualLeft ) , and dual-event alignment with stretch justification ( DualStretch ) . Differences between approaches were most pronounced with more rows of data . Secondly , we have proposed a phase-aware user modeling approach taking into account the user behavior change according to the event evolution over time . This user modeling approach comprises the following new novel aspects ( 1 ) Modeling microblog users behavior evolution by considering the different event phases ( 2 ) Characterizing users activity over time through a temporal sequence representation ( 3 ) Time-series-based selection of the most discriminative features characterizing users at each event phase . Thirdly , based on this proposed user modeling approach , we train various prediction models to learn to differentiate between prominent and non-prominent users behavior during crisis event . Our co-operative game is setup asymmetrically in terms of information sets for efficiency reasons . We develop and illustrate the framework in the context of temporal sequence models with examples . This paper presented a temporal sequence analyzing method , aiming at the extraction of typical sequences from an unlabeled dataset . The extraction procedure is based on HMM training and hierarchical separation of WTOM ( Weighted Transition Occurring Matrix ) . This paper is to analyse the typicality layer of Serial Verb Construction in Qiminyaoshu according to the similarity of semantic characters of prototypical Serial Verb Construction and comes to a conclusion that Temporal Sequence Principle , which divides the typicality layer , is the cognitive basis of Serial Verb Construction .
What is Bootstrapping ? Estimation Confidence Sets and Hypothesis Testing Regression Analysis Forecasting and Time Series Analysis Which Resampling Method Should You Use ?
In this paper we present the different approaches to modelling dynamic regression and structural equations• First , we give a survey of the traditional econometrie and the time series approaches to specification analysis of dynamic econometrie models . Then , we describe the three testing procedures that are presently available to the econometrician : ( 1 ) testing for misspecification or diagnostic checking , ( 2 " ) specification analysis or interpretive search , and ( 3 ) checking the overall consistency of the model .
However , some of the techniques used remains the same as that used in conventional data mining . As in the case of traditional data base systems , in time series data systems too the methods employed in capturing , indexing , representing and storing the data remains as the key issue . In time series data mining the indexing problem become very critical under the noisy conditions .
As wind variability is stochastic , Gaussian Process regression has recently been introduced to capture the randomness of wind energy . However , the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems . A variant Gaussian Process for time series forecasting is introduced in this study to address these issues .
The primary goal of this study was to propose an algorithm using mathematical programming to detect earnings management practices . In order to evaluate the ability of this proposed algorithm , the traditional statistical models are used as a benchmark vis-a-vis their time series counterparts . As emerging techniques in the area of mathematical programming yield better results , application of suitable models is expected to result in highly performed forecasts .
This paper improves Judicature De-noising Algorithm(JDNA ) in intelligent mechanical ventilation bed system . The judicature process is induced into the de-noising disposal of time series in algorithm , which builds a new de-nosing algorithm . On this basis , this paper optimizes key parameters that affect de-noising effects : illegal factor , the number of data used to formulate noise regulation , the first i legislative data weight , the number of largest evidence data .
On the basis of this idea , we proposed a new inference method that relies more on static gene expression data than time-series ones . Through the numerical experiments , we showed that the quality of the inferred genetic network is slightly improved by giving greater importance to static data than time-series ones . Although we develop the new method by modifying the random-forest-based inference method proposed by the authors , we could introduce the idea in this study into any inference method that is capable of analyzing both time-series and static gene expression data .
The problem of selecting the most appropriate , based on each specific query , distance measure out of a pool of measures for classification in time series data is also investigated . In addition , searching time series databases via examples , which may be either time series or models , is also part of this work . Moreover , motivated by the nature of music notes in the noisy QBH application , we explore the problem of interval-based sequence matching in a variety of application domains .
These universal properties make it possible to estimate how far a system is from a critical threshold . Several early-warning signals have been reported in time series representing systems near catastrophic shifts . The proper understanding of these early-warnings may allow the prediction and perhaps control of these dramatic shifts in a wide variety of systems .
In this paper , we present the approaches to identify the malicious users according to information integrity , behavior characteristics and conversation content in early time . Considering statistical methods , we propose the Time Series ( TIME ) model based on the analysis of users ' time behavior characteristics . And thinking from the theme of the users ' session , the improved Latent Dirichlet Allocation ( LDA ) malicious topic model is designed which has improved the sensitivity of the model to detect malicious topics .
Additional design considerations relate to compatibility with existing query languages , e.g. , the notion of temporal upward compatibility . However , as illustrated above , an additional hurdle stems from the fact that many ( early ) temporal query languages allowed the users to manipulate a finite underlying representation of temporal databases rather than the actual temporal values/objects in the associated temporal data model . A typical example of this situation would be an approach in which the temporal data model is based on time instants , while the query language introduces interval-valued attributes .
Our experiments entailed measuring the hand gestures of subjects for learning purposes and we used these data to create four-layered ANNs , which enabled the proposed system to successfully predict hand gestures in real time . A comparison of the proposed method with other algorithms using temporal data analysis suggested that the hand gesture prediction system using ANNs would be able to forecast various types of hand gestures using resistance data obtained from wearable devices based on PGSs .
We investigate issues pertaining to query processing of temporal databases in a relational environment . Tuple-versioning of relations is the adopted method of temporal data representation . New operators are necessary in order to exploit the richer semantics of temporal queries .
Secondly , we solved out the key technique of building temporal data topology , successfully developed a suit of spatio-temporal database management system adopting object-oriented methods . The system provides the temporal data collection , data storage , data management and data display and query functions . Finally , as a case study , we explored the application of spatio-temporal data management system with the administrative region data of multi-history periods of China as the basic data .
Different collection and processing tools , as well as several datasets of social media data have been developed and published as open-source software . The thesis posits that temporal analysis of data , from social media in particular , provides us with insights into real-world dynamics . Incorporating this temporal information into other applications can benefit standard tasks in natural language processing and beyond .
Frequent set mining ( FSM ) , an important phase of association rule mining , is the process of generating frequent sets that satisfy a specified minimum support threshold . This paper explores FSM in temporal data domain or FTP mining and proposes an efficient algorithm for the same . Existing algorithms for FTP mining are based on a priori 's level wise principle .
Integration of temporal concepts requires format definitions for temporal values as well as temporal predicates in the query language . The temporal data format is implemented using the GML temporal data type , i.e. , the format to define temporal data types for temporal attributes in the Nexus context model is based on the ISO 8601 standard for temporal values . Using temporal data , the identified main query aspect is concurrency , meaning any overlapping of two temporal values .
In order to reduce storage space and allow on-line query , how to trade off data compression effectiveness for on-line query performance is a challenge issue . In this paper , we are concerned with an effective framework for temporal data set that does not scarify on-line query performance and is specifically designed for very large sensor network database . The sampled data are compressed using several candidate approaches including dictionary-base compress and lossless vector quantization .
The process of extraction of unknown and new data from a huge dataset which is at times left during classification due to some undefined classification rules is known as novelty detection . We have used various level set methods in order to identify novelty data for temporal data time series . Novelty detection generally focused on the identification of shapes or different patterns , distance and learning based models , density based models , etc .
Inversely , these edges prevent grouping of multiple distant hypothesis of the same type , prevent merging two heads of different people . subsection : Temporal Model Regardless of the type of within frame model ( BU or TD / BU ) we rely on the same type of temporal edges that connect nodes of the same type in adjacent frames .
In , each refinement step is only indirectly influenced by previous iterations through the accumulation of error feedback . We claim that these refinement iterations should have a more direct and shared temporal representation . To remedy this , we introduce recurrent connections between each iteration ; specifically a long short term memory ( LSTM ) module .
Dynamic Bayesian Networks ( DBNs ) are important and widely used to model these sequential data , revealing inherent correlations between data of the systems . Under DBNs , Bayesian inference provides an optimal framework for sequential data analysis and produces solutions potentially better than other non Bayesian-based methods . Yet , intricate computations required by Bayesian inference are barrier to its application .
Their algorithm can be applied to any streaming data without requiring a training phase or a priori information , hence processing data on-the-fly and then discarding it . Therefore , the introduced algorithm is especially suitable for the applications requiring sequential data processing at large scales/high rates . The authors present a comprehensive experimental study in stationary and nonstationary environments .
These algorithms were simple and easily parallelizable in nature , which enabled them to achieve real-time performance . However , complex enhancement algorithms also require the sequential processing of data and this can not be easily achieved in real-time on a GPU . In this paper , the current advances in mobile CPU and GPU hardware are used to implement video enhancement algorithms in a new way on a mobile computer .
Anonymity is provided by utilizing only elements of the model that guarantee differential privacy . Furthermore , we develop a solution for generating differentially private sequential data , which will bring us one step closer to publicly available medical datasets via sequential data . We applied this method to two real medical events datasets and received some encouraging results , demonstrating that the proposed method can be used to publish high quality anonymized data .
In many practical settings one can sequentially and adaptively guide the collection of future data , based on information extracted from data collected previously . These sequential data collection procedures are known by different names , such as sequential experimental design , active learning , or adaptive sensing/sampling . The intricate relation between data analysis and acquisition in adaptive sensing paradigms can be extremely powerful , and often allows for reliable signal estimation and detection in situations where nonadaptive sensing would fail dramatically .
This paper gives a review of the most important methods for blood velocity vector flow imaging ( VFI ) for conventional sequential data acquisition . This includes multibeam methods , speckle tracking , transverse oscillation , color flow mapping derived VFI , directional beamforming , and variants of these .
The present study examines the relationship between the verbal communication skills and paraverbal communication ( both kinesic and proxemic ) of indoor soccer coaches in competitive match situations , senior women 's competitive level . T-patterns detection analysis provides a sequential analysis of data , illustrating the communicative style and flow of each coach .
Recurrent Neural Networks ( RNNs ) capture long dependencies and context , and 2 hence are the key component of typical sequential data based tasks . However , the sequential nature of RNNs dictates a large inference cost for long sequences even if the hardware supports parallelization .
In this chapter , we consider the hyperspectral classification problems in consideration of sequential data collection , which is a frequent setting in industrial pushboom imaging systems . We present related techniques including data normalization , dimension reduction , classification , and spatial information integration and the way to accommodate these techniques to the context of sequential data collecting and processing . The propose scheme is validated with real data collected in our laboratory .
Sequential LSTM models seem to learn syntactic structure from the natural language however their generalization on unseen text is relatively poor comparing with models that exploit syntactic tree structure . Unlike sequential models , recursive neural networks compose word phrases over syntactic tree structure and have shown improved performance in sentiment analysis . However its dependence on a syntactic tree architecture limits practical NLP applications .
subsection : Recurrent neural networks ( RNN ) Different from Artificial neural network ( ANN ) , RNN , a neural network with recurrent unit , has a better performance in solving many challenging problems involving sequential data analysis . The state of each time step of the recurrent unit is not only related to the input of the current step , but also related to the state of the previous step .
This paper presents a recurrent self-organizing map ( RSOM ) for temporal sequence processing . The RSOM uses the history of a pattern ( i.e. , the previous elements in the sequence ) to compute the best matching unit and to adapt the weights of the map .
Temporal sequence analysis is a pracrical technique popular in economic studies both at home and abroad . Based on the theories of econometrics and statistics , this article puts the data into the analysis frame for a purpose of coal price predication in the Chinese markets .
As a result of learning , a network is able to associate a pair of events with a temporal delay in a dynamic setting . The results demonstrate that the algorithm can also learn temporal sequence detection . Learning has also been tested in face-voice association using real biometric data .
For example performing a series of motor movements or recalling a sequence of episodic memories . Proposed networks which perform temporal sequence generation are often in the form of a modification to an auto-associative memory by using heteroassociative or time-varying synaptic strengths , requiring some pre-chosen temporal functions . Intra-modular synapses are trained auto-associatively with a Hebb rule , while a set of inter-module synapses are hetero-associative .
Furthermore , recently proposed dual-event alignment techniques have not been empirically evaluated . In this work , we designed tasks around temporal event sequence and timing analysis and conducted a controlled experiment on Amazon Mechanical Turk to examine four sentinel event alignment approaches : no sentinel event alignment ( NoAlign ) , single-event alignment ( SingleAlign ) , dual-event alignment with left justification ( DualLeft ) , and dual-event alignment with stretch justification ( DualStretch ) . Differences between approaches were most pronounced with more rows of data .
Secondly , we have proposed a phase-aware user modeling approach taking into account the user behavior change according to the event evolution over time . This user modeling approach comprises the following new novel aspects ( 1 ) Modeling microblog users behavior evolution by considering the different event phases ( 2 ) Characterizing users activity over time through a temporal sequence representation ( 3 ) Time-series-based selection of the most discriminative features characterizing users at each event phase . Thirdly , based on this proposed user modeling approach , we train various prediction models to learn to differentiate between prominent and non-prominent users behavior during crisis event .
Our co-operative game is setup asymmetrically in terms of information sets for efficiency reasons . We develop and illustrate the framework in the context of temporal sequence models with examples .
This paper presented a temporal sequence analyzing method , aiming at the extraction of typical sequences from an unlabeled dataset . The extraction procedure is based on HMM training and hierarchical separation of WTOM ( Weighted Transition Occurring Matrix ) .
This paper is to analyse the typicality layer of Serial Verb Construction in Qiminyaoshu according to the similarity of semantic characters of prototypical Serial Verb Construction and comes to a conclusion that Temporal Sequence Principle , which divides the typicality layer , is the cognitive basis of Serial Verb Construction .
Bootstrap Methods: A Practitioner's Guide Structural econometric modelling and time series analysis towards an integrated approach Forecasting of economic data sets and there trends using time series data modeling Time series wind power forecasting based on variant Gaussian Process and TLBO The detection of earnings manipulation: the three‐phase cutting plane algorithm using mathematical programming Improvement of Judicature De-noising Algorithm and Optimization of Key Parameters [Regular Paper] Inference of Genetic Networks Using Random Forests: Use of Different Weights for Time-Series and Static Gene Expression Data Online Efficient And Effective Search In Large And Noisy Sequence Databases Behavior of Early Warnings near the Critical Temperature in the Two-Dimensional Ising Model Detecting Malicious Users in Online Dating Application Temporal Database Entries for the Springer Encyclopedia of Database Systems Gesture Prediction Using Wearable Sensing Systems with Neural Networks for Temporal Data Analysis A Framework for Query Optimization in Temporal Databases Research on spatio-temporal database techniques for spatial information service Temporal models of streaming social media data An m-ary tree based Frequent Temporal Pattern (FTP) mining algorithm Verwaltung von zeitbezogenen Daten und Sensordatenströmen Data compression and query for large scale sensor data on COTS DBMS A heuristic approach to detect novelty data using improved level set methods ArtTrack: Articulated Multi-Person Tracking in the Wild Towards Viewpoint Invariant 3D Human Pose Estimation Expectation propagation algorithm for bayesian inference in dynamic systems Online Classification via Self-Organizing Space Partitioning Using the CPU and GPU for real-time video enhancement on a mobile computer Publishing Differentially Private Medical Events Data Adaptive Sensing for Estimation of Structured Sparse Signals Ultrasound Vector Flow Imaging—Part I: Sequential Systems Observing the paraverbal communication of coaches in competitive match situations Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices Sequential Classification of Hyperspectral Images Neural Tree Indexers for Text Understanding Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification A Recurrent Self-Organizing Map for Temporal Sequence Processing Application of Temporal Sequence Analysis Technique in Coal Price Prediction Spatio-temporal event association using reward-modulated spike-time-dependent plasticity A multi-modular associator network for simple temporal sequence learning and generation Evaluating Alignment Approaches in Superimposed Time-Series and Temporal Event-Sequence Visualizations Prominent microblog users prediction during crisis events : using phase-aware and temporal modeling of users behavior. (Prédiction des utilisateurs primordiaux des microblogs durant les situations de crise : modélisation temporelle des comportements des utilisateurs en fonction des phases des évènem Game-Theoretic Interpretability for Temporal Modeling Typical Sequences Extraction and Recognition Typicality layer of serial verb construction in Qiminyaoshu based on the prototype semantic feature
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BigCom SSDBM ETFA 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV CD-ARES MB NeurIPS EACL ArXiv ICANN ESANN ECCV Workshop on HCI
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