Mining Complex Time-Series Data by Learning Markovian Models

2006 ◽  
Author(s):  
Yi Wang ◽  
Lizhu Zhou ◽  
Jianhua Feng ◽  
Jianyong Wang ◽  
Zhi-qiang Liu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Complex Time ◽  
Markovian Models ◽  
Mining Complex

Statistical Inference for Complex Time Series Data

2013 ◽  
Vol 10 (3) ◽  
pp. 2749-2823
Author(s):  
Rainer Dahlhaus ◽  
Oliver Linton ◽  
Wei-Biao Wu ◽  
Qiwei Yao
Keyword(s):  
Time Series ◽  
Statistical Inference ◽  
Time Series Data ◽  
Series Data ◽  
Complex Time

AN INTEGRATED DATA CHARACTERISTIC TESTING SCHEME FOR COMPLEX TIME SERIES DATA EXPLORATION

2013 ◽  
Vol 12 (03) ◽  
pp. 491-521 ◽  
Author(s):  
LING TANG ◽  
LEAN YU ◽  
FANGTAO LIU ◽  
WEIXUAN XU
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Data Exploration ◽  
Series Data ◽  
Economic Time Series ◽  
Noise Pattern ◽  
Complex Time ◽  
Testing Scheme ◽  
Sample Data ◽  
Economic Time

In this paper, an integrated data characteristic testing scheme is proposed for complex time series data exploration so as to select the most appropriate research methodology for complex time series modeling. Based on relationships across different data characteristics, data characteristics of time series data are divided into two main categories: nature characteristics and pattern characteristics in this paper. Accordingly, two relevant tasks, nature determination and pattern measurement, are involved in the proposed testing scheme. In nature determination, dynamics system generating the time series data is analyzed via nonstationarity, nonlinearity and complexity tests. In pattern measurement, the characteristics of cyclicity (and seasonality), mutability (or saltation) and randomicity (or noise pattern) are measured in terms of pattern importance. For illustration purpose, four main Chinese economic time series data are used as testing targets, and the data characteristics hidden in these time series data are thoroughly explored by using the proposed integrated testing scheme. Empirical results reveal that the natures of all sample data demonstrate complexity in the phase of nature determination, and in the meantime the main pattern of each time series is captured based on the pattern importance, indicating that the proposed scheme can be used as an effective data characteristic testing tool for complex time series data exploration from a comprehensive perspective.

scholarly journals Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xue-Bo Jin ◽  
Jia-Hui Zhang ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong ◽  
...  
Keyword(s):  
Time Series ◽  
Wavelet Decomposition ◽  
Time Series Data ◽  
Bayesian Optimization ◽  
Data Driven ◽  
Practical Significance ◽  
Series Data ◽  
Complex Time ◽  
Modeling And Analysis ◽  
Multicomponent Data

Complex time series data exists widely in actual systems, and its forecasting has great practical significance. Simultaneously, the classical linear model cannot obtain satisfactory performance due to nonlinearity and multicomponent characteristics. Based on the data-driven mechanism, this paper proposes a deep learning method coupled with Bayesian optimization based on wavelet decomposition to model the time series data and forecasting its trend. Firstly, the data is decomposed by wavelet transform to reduce the complexity of the time series data. The Gated Recurrent Unit (GRU) network is trained as a submodel for each decomposition component. The hyperparameters of wavelet decomposition and each submodel are optimized with Bayesian sequence model-based optimization (SMBO) to develop the modeling accuracy. Finally, the results of all submodels are added to obtain forecasting results. The PM2.5 data collected by the US Air Quality Monitoring Station is used for experiments. By comparing with other networks, it can be found that the proposed method outperforms well in the multisteps forecasting task for the complex time series.

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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