scholarly journals Motivic Pattern Extraction in Music, and Application to the Study of Tunisian Modal Music

2007 ◽  
Vol Volume 6, april 2007, joint... ◽  
Author(s):  
Olivier Lartillo ◽  
Mondher Ayari
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Basic Mechanism ◽  
Series Data ◽  
Periodic Pattern ◽  
Global Pattern ◽  
Basic Principles ◽  
Discovery System ◽  
International Audience ◽  
Modal Music

International audience A new methodology for automated extraction of repeated patterns in time-series data is presented, aimed in particular at the analysis of musical sequences. The basic principles consists in a search for closed patterns in a multi-dimensional parametric space. It is shown that this basic mechanism needs to be articulated with a periodic pattern discovery system, implying therefore a strict chronological scanning of the time-series data. Thanks to this modelling global pattern filtering may be avoided and rich and highly pertinent results can be obtained. The modelling has been integrated in a collaborative pro ject between ethnomusicology, cognitive sciences and computer science, aimed at the study of Tunisian Modal Music. Une méthodologie d'extraction automatique de motifs répétés dans des séquences temporelles est présentée, dédiée en particulier à l'analyse de séquences musicales. L'approche initiale consiste en une recherche de motifs fermés dans un espace paramétrique multidimensionnel. Il est montré que ce premier mécanisme doit être articulé avec un système de découverte de motifs périodiques, ce qui implique un parcours strictement chronologique de la séquence. Cette modélisation permet d'éviter un filtrage global des patterns, et donc d'obtenir des résultats présentant une richesse et une pertinence élevée. La modélisation a été intégrée au sein d'un projet collaboratif entre ethnomusicologie, sciences cognitives et informatique, dédié à l'étude de la musique modale tunisienne.

scholarly journals Construction and Simulation of Economic Statistics Measurement Model Based on Time Series Analysis and Forecast

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lu Xu ◽  
Weijie Chen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Arima Model ◽  
Series Data ◽  
Forecasting Model ◽  
Basic Principles ◽  
Series Analysis ◽  
Model Based ◽  
Combined Forecasting

Time series follow the basic principles of mathematical statistics and can provide a set of scientifically based dynamic data processing methods. Using this method, various types of data can be approximated by corresponding mathematical models, and then, the internal structure and complex characteristics of the data can be understood essentially, so as to achieve the purpose of predicting its development trend. This paper mainly studies the combined forecasting model based on the time series model and its application. First, the application prospects and research status of the combined forecasting model, the source of time series analysis, and the status of research development at home and abroad are given, and the purpose and significance of the research topic and the research content are summarized. Then, the paper gives the relevant theories about the ARIMA model and the basic principles of model recognition and explains the method of time series smoothing. Finally, the paper uses the ARIMA model to identify and fit the time series data and then the gray forecast model to fit and predict the time series data. Finally, by assigning reasonable weights and combining these methods, a combined forecasting model is proposed and carried out.

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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