scholarly journals Chebyshev Similarity Match between Uncertain Time Series

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Wang ◽  
Guohua Liu ◽  
Dingjia Liu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Computational Cost ◽  
Random Variable ◽  
Repeated Measurements ◽  
Series Data ◽  
Chebyshev Inequality ◽  
Central Tendency ◽  
Uncertain Time ◽  
Estimation Interval

In real application scenarios, the inherent impreciseness of sensor readings, the intentional perturbation of privacy-preserving transformations, and error-prone mining algorithms cause much uncertainty of time series data. The uncertainty brings serious challenges for the similarity measurement of time series. In this paper, we first propose a model of uncertain time series inspired by Chebyshev inequality. It estimates possible sample value range and central tendency range in terms of sample estimation interval and central tendency estimation interval, respectively, at each time slot. In comparison with traditional models adopting repeated measurements and random variable, Chebyshev model reduces overall computational cost and requires no prior knowledge. We convert Chebyshev uncertain time series into certain time series matrix; therefore noise reduction and dimensionality reduction are available for uncertain time series. Secondly, we propose a new similarity matching method based on Chebyshev model. It depends on overlaps between two sample estimation intervals and overlaps between central tendency estimation intervals from different uncertain time series. At the end of this paper, we conduct an extensive experiment and analyze the results by comparing with prior works.

scholarly journals Fuzzy Prediction Intervals Using Credibility Distributions

2021 ◽  
Vol 5 (1) ◽  
pp. 51
Author(s):  
Enriqueta Vercher ◽  
Abel Rubio ◽  
José D. Bermúdez
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Computational Cost ◽  
Expected Value ◽  
Prediction Intervals ◽  
Series Data ◽  
Comparative Results ◽  
Automatic Forecasting

We present a new forecasting scheme based on the credibility distribution of fuzzy events. This approach allows us to build prediction intervals using the first differences of the time series data. Additionally, the credibility expected value enables us to estimate the k-step-ahead pointwise forecasts. We analyze the coverage of the prediction intervals and the accuracy of pointwise forecasts using different credibility approaches based on the upper differences. The comparative results were obtained working with yearly time series from the M4 Competition. The performance and computational cost of our proposal, compared with automatic forecasting procedures, are presented.

scholarly journals Time-Series Classification Based on Fusion Features of Sequence and Visualization

2020 ◽  
Vol 10 (12) ◽  
pp. 4124
Author(s):  
Baoquan Wang ◽  
Tonghai Jiang ◽  
Xi Zhou ◽  
Bo Ma ◽  
Fan Zhao ◽  
...  
Keyword(s):  
Time Series ◽  
Human Brain ◽  
Time Series Data ◽  
Short Term Memory ◽  
Computational Cost ◽  
Open Data ◽  
Attention Mechanism ◽  
Series Data ◽  
Sequence Features ◽  
Fusion Features

For the task of time-series data classification (TSC), some methods directly classify raw time-series (TS) data. However, certain sequence features are not evident in the time domain and the human brain can extract visual features based on visualization to classify data. Therefore, some researchers have converted TS data to image data and used image processing methods for TSC. While human perceptionconsists of a combination of human senses from different aspects, existing methods only use sequence features or visualization features. Therefore, this paper proposes a framework for TSC based on fusion features (TSC-FF) of sequence features extracted from raw TS and visualization features extracted from Area Graphs converted from TS. Deep learning methods have been proven to be useful tools for automatically learning features from data; therefore, we use long short-term memory with an attention mechanism (LSTM-A) to learn sequence features and a convolutional neural network with an attention mechanism (CNN-A) for visualization features, in order to imitate the human brain. In addition, we use the simplest visualization method of Area Graph for visualization features extraction, avoiding loss of information and additional computational cost. This article aims to prove that using deep neural networks to learn features from different aspects and fusing them can replace complex, artificially constructed features, as well as remove the bias due to manually designed features, in order to avoid the limitations of domain knowledge. Experiments on several open data sets show that the framework achieves promising results, compared with other methods.

scholarly journals Time Series Classification by Shapelet Dictionary Learning with SVM-Based Ensemble Classifier

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jitao Zhang ◽  
Weiming Shen ◽  
Liang Gao ◽  
Xinyu Li ◽  
Long Wen
Keyword(s):  
Time Series ◽  
Dictionary Learning ◽  
Time Series Data ◽  
Computational Cost ◽  
Ensemble Classifier ◽  
Series Data ◽  
Features Selection ◽  
Time Series Classification ◽  
Generalization Ability ◽  
Discovery Method

Time series classification is a basic and important approach for time series data mining. Nowadays, more researchers pay attention to the shape similarity method including Shapelet-based algorithms because it can extract discriminative subsequences from time series. However, most Shapelet-based algorithms discover Shapelets by searching candidate subsequences in training datasets, which brings two drawbacks: high computational burden and poor generalization ability. To overcome these drawbacks, this paper proposes a novel algorithm named Shapelet Dictionary Learning with SVM-based Ensemble Classifier (SDL-SEC). SDL-SEC modifies the Shapelet algorithm from two aspects: Shapelet discovery method and classifier. Firstly, a Shapelet Dictionary Learning (SDL) is proposed as a novel Shapelet discovery method to generate Shapelets instead of searching them. In this way, SDL owns the advantages of lower computational cost and higher generalization ability. Then, an SVM-based Ensemble Classifier (SEC) is developed as a novel ensemble classifier and adapted to the SDL algorithm. Different from the classic SVM that needs precise parameters tuning and appropriate features selection, SEC can avoid overfitting caused by a large number of features and parameters. Compared with the baselines on 45 datasets, the proposed SDL-SEC algorithm achieves a competitive classification accuracy with lower computational cost.

scholarly journals Approaches of Handling Uncertain Time Series Data towards Prediction

2016 ◽  
Vol 5 (6) ◽  
pp. 233-236
Author(s):  
Radzuan M. F. Nabilah ◽  
◽  
Zalinda Othman ◽  
Bakar A. Azuraliza
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Uncertain Time

Variance error of multi-classification based anomaly detection for time series data

2020 ◽  
pp. 1-16
Author(s):  
Baoquan Wang ◽  
Tonghai Jiang ◽  
Xi Zhou ◽  
Bo Ma ◽  
Fan Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Short Term Memory ◽  
Computational Cost ◽  
Reconstruction Error ◽  
Detection Methods ◽  
Series Data ◽  
Data Set

For abnormal detection of time series data, the supervised anomaly detection methods require labeled data. While the range of outlier factors used by the existing semi-supervised methods varies with data, model and time, the threshold for determining abnormality is difficult to obtain, in addition, the computational cost of the way to calculate outlier factors from other data points in the data set is also very large. These make such methods difficult to practically apply. This paper proposes a framework named LSTM-VE which uses clustering combined with visualization method to roughly label normal data, and then uses the normal data to train long short-term memory (LSTM) neural network for semi-supervised anomaly detection. The variance error (VE) of the normal data category classification probability sequence is used as outlier factor. The framework enables anomaly detection based on deep learning to be practically applied and using VE avoids the shortcomings of existing outlier factors and gains a better performance. In addition, the framework is easy to expand because the LSTM neural network can be replaced with other classification models. Experiments on the labeled and real unlabeled data sets prove that the framework is better than replicator neural networks with reconstruction error (RNN-RS) and has good scalability as well as practicability.

Recognition of workers’ actions from time-series signal images using deep convolutional neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Omobolanle Ruth Ogunseiju ◽  
Johnson Olayiwola ◽  
Abiola Abosede Akanmu ◽  
Chukwuma Nnaji
Keyword(s):  
Time Series ◽  
Action Recognition ◽  
Nearest Neighbor ◽  
Time Series Data ◽  
Computational Cost ◽  
Construction Workers ◽  
Series Data ◽  
K Nearest Neighbor ◽  
Deep Convolutional Neural Networks ◽  
Content Type

PurposeConstruction action recognition is essential to efficiently manage productivity, health and safety risks. These can be achieved by tracking and monitoring construction work. This study aims to examine the performance of a variant of deep convolutional neural networks (CNNs) for recognizing actions of construction workers from images of signals of time-series data.Design/methodology/approachThis paper adopts Inception v1 to classify actions involved in carpentry and painting activities from images of motion data. Augmented time-series data from wearable sensors attached to worker's lower arms are converted to signal images to train an Inception v1 network. Performance of Inception v1 is compared with the highest performing supervised learning classifier, k-nearest neighbor (KNN).FindingsResults show that the performance of Inception v1 network improved when trained with signal images of the augmented data but at a high computational cost. Inception v1 network and KNN achieved an accuracy of 95.2% and 99.8%, respectively when trained with 50-fold augmented carpentry dataset. The accuracy of Inception v1 and KNN with 10-fold painting augmented dataset is 95.3% and 97.1%, respectively.Research limitations/implicationsOnly acceleration data of the lower arm of the two trades were used for action recognition. Each signal image comprises 20 datasets.Originality/valueLittle has been reported on recognizing construction workers' actions from signal images. This study adds value to the existing literature, in particular by providing insights into the extent to which a deep CNN can classify subtasks from patterns in signal images compared to a traditional best performing shallow network.

scholarly journals Human Activity Prediction in Smart Home Time Series Data by using Change Point Detection

2020 ◽  
Vol 9 (5) ◽  
pp. 1268-1271
Keyword(s):  
Time Series ◽  
Change Point ◽  
Time Series Data ◽  
Proper Time ◽  
Arima Model ◽  
Computational Cost ◽  
Change Point Detection ◽  
Series Data ◽  
Detection Accuracy ◽  
Point Detection

A Sensor is attached to a house which send message now and then about the housemates or the old people activities. Whether they had taken food in the correct time or not and other activities they perform in the proper time or not. Change Point Detection (CPD) is a matter of perspective which discovers deviating from what are normal or usual changes within the housemates. Any abnormal changes in the housemates have been identifying the presence of time points. The dissimilar changes occurs called SEPERATION Change Point Detection. It will not coincide when a remarkable occurrence of events at any points. Change Point Detection (CPD) occurs at the same time and in the problem of finding unexpected changes in facts and statistics collected together for references and analysis and in the property of the time series changes. An unusual real-time not involving any assumptions as to the form or in the parameters of a frequency distribution change point detection algorithm called Separation , It is used to calculate as a parting to recognize change points in fully measurement characteristics relating to measurements having sufficient depth and substance to be in time series. To ameliorate the order of this algorithm used in ARIMA with SEP algorithm. ARIMA model is used for predicting the Time series forecasting result. If emergency is occur then automatically send notification to caring person. The proposed work can decreasing computational cost and also improves the detection accuracy in the quality or fact of being useful of proposed technique.

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