Eigen analysis of the stability and degree of information content in correlation matrices constructed from property time series data

AbstractThis article tests the stability of the main hedonic wine price coefficients over time. We draw on an extensive literature review to identify the most frequently used methodology and define a standard hedonic model. We estimate this model on monthly subsamples of a worldwide auction database of the most commonly exchanged fine wines. This provides, for each attribute, a monthly time series of hedonic coefficients time series data from 2003 to 2014. Using a multivariate autoregressive model, we then study the stability of these coefficients over time and test the existence of structural or cyclical changes related to fluctuations in general price levels. We find that most hedonic coefficients are variable and either exhibit structural or cyclical variations over time. These findings shed doubt on the relevance of both short- and long-run hedonic estimations. (JEL Classifications: C13, C22, D44, G11)

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ANALISIS PENGARUH VARIABEL MAKROEKONOMI DAN COUNTRY RISK TERHADAP INVESTASI PORTOFOLIO ASING DI INDONESIA

Ecosains: Jurnal Ilmiah Ekonomi dan Pembangunan ◽

10.24036/ecosains.11066657.00 ◽

2018 ◽

Vol 7 (2) ◽

pp. 135

Author(s):

Halifah Hadi ◽

Hasdi Aimon ◽

Dewi Zaini Putri

Keyword(s):

Economic Growth ◽

Time Series ◽

Time Series Data ◽

Country Risk ◽

Growth Effect ◽

Series Data ◽

Short Term ◽

Correction Model ◽

The Stability

The reseach aims to explain the effect of country risk and variabels macroeconomics to the foreign portofolio invesment in Indonesia in short term and long term. The analysis takes time series time series data from 2006 quarter 1 through 2016 quarter 4by using Error Correction Model (ECM). The source of data are Badan Pusat Statistik, Bank Indonesia, FX Sauder and World Bank. The result are in the short term the exchange rate and economic growth effect the shock that will influence the foreign portofolio invesment. In the long trem the inflation, interst rate, money supply and country risk influence on foreign portofolio invesment significanly. The suggestion in this research is, the goverment sould keep the stability balance of payment in Indonesia .Any change, the condition of balance of payments effect appreciation and depreciation to Rupiah. To increase the economic growth in Indonesia, goverment could increasing the fiscal income and PMDN realization that will increase the enterprises productivity.

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Investigation of Combustion Instability in a Swirl-Stabilized Combustor Using Symbolic Time Series Analysis

ASME 2014 Gas Turbine India Conference ◽

10.1115/gtindia2014-8280 ◽

2014 ◽

Cited By ~ 2

Author(s):

Vikram Ramanan ◽

S. R. Chakravarthy ◽

Soumalya Sarkar ◽

Ashok Ray

Keyword(s):

Time Series ◽

Fuel Injection ◽

Time Series Data ◽

Combustion Instability ◽

Series Data ◽

Pressure Amplitude ◽

Finite State ◽

Symbolic Dynamic Filtering ◽

Dynamic Filtering ◽

The Stability

A laboratory-scale swirl-stabilized combustor is experimentally characterized for various configurations involving variable air flow rates and different fuel injection locations. Unsteady pressure and heat release rate measurements were obtained simultaneously in order to determine the stability map of the combustor for the experimented configurations. It is observed that a sharp rise in pressure amplitude coincides with a break in the dominant spectral content variation with the inlet Reynolds number. The time series data were analyzed by using the tools of symbolic dynamic filtering and the divergences among the outputs of each sub-class of observations were obtained as anomaly measures. In the proposed method, symbol strings are generated by partitioning the (finite-length) time series to construct a special class of probabilistic finite state automata (PFSA) that have a deterministic algebraic structure. The anomaly measures are defined based on the probabilistic state vectors distribution across each sub class. The method which is based on representing a given time series data as a set of PFSA is observed to be capable of predicting an impending combustion instability as well as to distinguish between the symbol-state distribution among various instability conditions. The measure also successfully captures changes in the thermoacoustic regime as a function of the fuel injection location.

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Atmospheric Correction Thresholds for Ground-Based Radar Interferometry Deformation Monitoring Estimated Using Time Series Analyses

Remote Sensing ◽

10.3390/rs12142236 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2236 ◽

Cited By ~ 1

Author(s):

Tomasz Owerko ◽

Przemysław Kuras ◽

Łukasz Ortyl

Keyword(s):

Time Series ◽

Satellite Data ◽

Time Series Data ◽

Learning Algorithm ◽

Atmospheric Correction ◽

Deformation Monitoring ◽

Radar Interferometry ◽

Series Data ◽

Atmospheric Conditions ◽

The Stability

Ground-based radar interferometry (GBSAR) is a useful method to control the stability of engineering objects and elements of geographical spaces at risk of deformation or displacement. To secure accurate and credible measurement results, it is crucial to consider atmospheric conditions as they influence the corrections to distance measurements. These conditions are especially important considering the radar bandwidth used. Measurements for the stability of engineering objects are not always performed in locations where meteorological monitoring is prevalent; however, information about the range of variability in atmospheric corrections is always welcome. The authors present a hybrid method to estimate the probable need of atmospheric corrections, which allows partly eliminating false positive alarms of deformations as caused by atmospheric fluctuations. Unlike the numerous publications on atmospheric reductions focused on the current state of the atmosphere, the proposed solution is based on applying a classic machine learning algorithm designed for the SARIMAX (Seasonal Autoregressive Integrated Moving Average with covariate at time) time series data model for satellite data shared by NASA (National Aeronautics and Space Administration) during the Landsat MODIS (Moderate Resolution Imaging Spectroradiometer) mission before performing residual estimation during the monitoring phase. Example calculations (proof of concept) were made for ten-year satellite data covering a region for experimental flood bank stability observations as performed using the IBIS-L (Image by Interferometric Survey—Landslide) radar and for target monitoring data (ground measurements).

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Detecting Traffic Incidents Using Persistence Diagrams

Algorithms ◽

10.3390/a13090222 ◽

2020 ◽

Vol 13 (9) ◽

pp. 222 ◽

Cited By ~ 1

Author(s):

Eric S. Weber ◽

Steven N. Harding ◽

Lee Przybylski

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Time Series Data ◽

Series Data ◽

Traffic Data ◽

Traffic Incidents ◽

Data Point ◽

The Stability ◽

Persistence Diagrams

We introduce a novel methodology for anomaly detection in time-series data. The method uses persistence diagrams and bottleneck distances to identify anomalies. Specifically, we generate multiple predictors by randomly bagging the data (reference bags), then for each data point replacing the data point for a randomly chosen point in each bag (modified bags). The predictors then are the set of bottleneck distances for the reference/modified bag pairs. We prove the stability of the predictors as the number of bags increases. We apply our methodology to traffic data and measure the performance for identifying known incidents.

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The Modelling of Heteroscedastics IDR-USD Exchange Rate with Intervention and Outlier Factors

Journal of Physics Conference Series ◽

10.1088/1742-6596/2084/1/012002 ◽

2021 ◽

Vol 2084 (1) ◽

pp. 012002

Author(s):

Utriweni Mukhaiyar ◽

Dhika Yudistira ◽

Sapto Wahyu Indratno ◽

Wan Fairos Wan Yaacob

Keyword(s):

Time Series ◽

Exchange Rate ◽

Time Series Data ◽

Arima Model ◽

Series Data ◽

Estimation Result ◽

Data Modelling ◽

The Stability

Abstract The nonstationary in time series data may be caused by the existence of intervention, outliers, and heteroscedastic effects. The outliers can represent an intervention so that it creates a heteroscedastic process. This research investigates the involvements of these three factors in time series data modelling. It is also reviewed how long the effects of the intervention and outliersfactors will last. The weekly IDR-USD exchange rate in period of May 2015 to April 2020 be evaluated. It is obtained that ARIMA model with the intervention factor gives the best re-estimation result, with smallest average of errors squared. Meanwhile for prediction, the heteroscedastic effect combined with outlier factors gives better results with the lowest percentage of errors. One of the phenomenal interventions in this data is the Covid-19 pandemic, which was started in Indonesia on March 2020. It is found that the effect of the intervention lasts less than five months and the prediction shows that the volatility of IDR-USD exchange rate starts to decline. This shows the stability of the process is starting to be maintained.

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ALTERNATIF PENGGEROMBOLAN DATA DERET WAKTU DENGAN KONDISI TERDAPAT DATA KOSONG

Indonesian Journal of Statistics and Its Applications ◽

10.29244/ijsa.v2i1.55 ◽

2018 ◽

Vol 2 (1) ◽

pp. 13-22

Author(s):

Yusma Yanti ◽

Septian Rahardiantoro

Keyword(s):

Time Series ◽

Missing Data ◽

Time Series Data ◽

Series Data ◽

Correlation Matrices ◽

Alternative Method ◽

Data Value ◽

Ratio Data ◽

Different Characteristics ◽

Better Than

Panel data describes a condition in which there are many observations with each observation observed periodically over a period of time. The observation clustering context based on this data is known as Clustering of Time Series Data. Many methods are developed based on fluctuating time series data conditions. However, missing data causes problems in this analysis. Missing data is the unavailability of data value on an observation because there is no information related to it. This study attempts to provide an alternative method of clustering observations on data with time series containing missing data by utilizing correlation matrices converted into Euclid distance matrices which are subsequently applied by the hierarchical clustering method. The simulation process was done to see the goodness of alternative method with common method used in data with 0%, 10%, 20% and 40% missing data condition. The result was obtained that the accuracy of the observation bundling on the proposed alternative method is always better than the commonly used method. Furthermore, the implementation was done on the annual gini ratio data of each province in Indonesia in 2007 to 2017 which contained missing data in North Kalimantan Province. There were 2 clusters of province with different characteristics.

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

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

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

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

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.

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