THE INTERPOLATION ERROR OPERATOR: TIME SERIES ERROR DETECTION AND CORRECTION

Geophysics ◽  
1977 ◽  
Vol 42 (4) ◽  
pp. 773-777 ◽  
Author(s):  
Douglas Nyman
Keyword(s):  
Time Series ◽  
Error Detection ◽  
Time Series Data ◽  
Interpolation Error ◽  
Series Data ◽  
Interpolation Operator ◽  
Data Errors ◽  
Error Detection And Correction ◽  
Digital Time ◽  
Erroneous Data

The interpolation error operator is an effective tool for the detection of digital time series data errors characterized by discontinuities in the function itself or its low order derivatives. The generalized interpolation operator of multiplicity n simultaneously interpolates n consecutive terms. This operator is effective in correcting short lengths of erroneous data.

Watermarking Audio Signals for Copyright Protection Using ICA

2010 ◽  
pp. 144-157 ◽  
Author(s):  
B. R. Matam ◽  
David Lowe
Keyword(s):  
Time Series ◽  
Copyright Protection ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Audio Signals ◽  
Copyright Information ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Digital Time

The protection or tracking of content transmitted via digital time series data is an important and under-researched area of watermarking. In this chapter a discussion of information hiding in the context of copyright protection of audio signals, an example of time series data is presented. Independent component analysis (ICA) based watermarking methods are used to embed copyright information. The integrity of a hidden message when the cover text in which it is hidden, is attacked by applying signal processing techniques such as filtering and addition of noise to the signal will be investigated. The results of the application of the ICA based method are compared with the results of the application of the discrete wavelet transform (DWT) based approach. This chapter reveals the advantages of using a data dependent transform (for example ICA) based watermarking method for copyright applications when compared with static transform domain (having fixed coefficients, for example DWT) based methods.

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

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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