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

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 Fast Online Similarity Search for Uncertain Time Series

2020 ◽  
Vol 28 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Ruizhe Ma ◽  
Diwei Zheng ◽  
Li Yan
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Index Structure ◽  
Series Data ◽  
Retrieval Algorithm ◽  
Online Data ◽  
Distance Threshold ◽  
Positive Ratio ◽  
Online Processing ◽  
Uncertain Time

To achieve fast retrieval of online data, it is needed for the retrieval algorithm to increase throughput while reducing latency. Based on the traditional online processing algorithm for time series data, we propose a spatial index structure that can be updated and searched quickly in a real-time environment. At the same time, we introduce an adaptive segmentation method to divide the space corresponding to nodes. Unlike traditional retrieval algorithms, for uncertain time series, the distance threshold used for screening will dynamically change due to noise during the search process. Extensive experiments are conducted to compare the accuracy of the query results and the timeliness of the algorithm. The results show that the index structure proposed in this paper has better efficiency while maintaining a similar true positive ratio.

scholarly journals A Hybrid Approach for Clustering Uncertain Time Series

2021 ◽  
Vol 28 (4) ◽  
pp. 255-267
Author(s):  
Ruizhe Ma ◽  
Xiaoping Zhu ◽  
Li Yan
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Knowledge Engineering ◽  
Uncertain Data ◽  
Hybrid Approach ◽  
Series Data ◽  
Adjusted Rand Index ◽  
Clustering Approach ◽  
Uncertain Time

Information uncertainty extensively exists in the real-world applications, and uncertain data process and analysis have been a crucial issue in the area of data and knowledge engineering. In this paper, we concentrate on uncertain time series data clustering, in which the uncertain values at time points are represented by probability density function. We propose a hybrid clustering approach for uncertain time series. Our clustering approach first partitions the uncertain time series data into a set of micro-clusters and then merges the micro-clusters following the idea of hierarchical clustering. We evaluate our approach with experiments. The experimental results show that, compared with the traditional UK-means clustering algorithm, the Adjusted Rand Index (ARI) of our clustering results have an obviously higher accuracy. In addition, the time efficiency of our clustering approach is significantly improved.

Uncertain Autoregressive Model via LASSO Procedure

2020 ◽  
Vol 28 (06) ◽  
pp. 939-956
Author(s):  
Ziqian Zhang ◽  
Xiangfeng Yang ◽  
Jinwu Gao
Keyword(s):  
Time Series ◽  
Autoregressive Model ◽  
Time Series Data ◽  
Series Data ◽  
Temporal Dependence ◽  
Basic Model ◽  
Final Prediction Error ◽  
Optimum Order ◽  
Selection Operator ◽  
Uncertain Time

Uncertain time series analysis is a method to predict future values based on imprecisely observed values. As a basic model of uncertain time series, an uncertain autoregressive model has been presented. However, the existing paper ignores the temporal dependence information embedded in time-series data. In dealing with this issue, this paper adds a least absolute shrinkage and selection operator penalty to the traditional uncertain autoregressive model and selects the optimum order of the model according to Akaike’s final prediction error criterion. Finally, two numerical examples are given to illustrate the effectiveness of the model and compare the results predicted by the uncertain autoregressive model with the principle of least squares.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data
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