scholarly journals Monitoring Subsidence Deformation of Suzhou Subway Using InSAR Timeseries Analysis

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 3400-3416
Author(s):  
Xiaobo Xu ◽  
Dezheng Zhao ◽  
Chao Ma ◽  
Dajun Lian
Keyword(s):  
Timeseries Analysis

scholarly journals Systematic illiquidity, characteristic illiquidity, and stock returns: Timeseries analysis

2022 ◽  
Vol 9 (2) ◽  
pp. 72-80
Author(s):  
Soltane et al. ◽  
Keyword(s):  
Emerging Markets ◽  
Stock Returns ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Systematic Risk ◽  
Timeseries Analysis ◽  
The Relationship

The objective of this research is to investigate the relationship between illiquidity and stock prices on the Tunisian stock exchange. While previous researches tended to focus on one form of illiquidity to examine this relationship, our study unifies three forms of illiquidity at the same time. Indeed, we simultaneously consider illiquidity as systematic risk, as a characteristic of the market, and as a characteristic of the stock. The aggregate illiquidity of the market is the average of individual stock illiquidity. The illiquidity risk is the sensitivity of the stock price to illiquidity shocks. Shocks of market illiquidity are estimated by the innovations in the expected market illiquidity. Results show that investors on the Tunisian stock exchange do not require higher returns when they expect a rise of market illiquidity, whereas investors on U.S markets are compensated for higher expected market illiquidity. In addition, shocks of market illiquidity provoke a fall in stock prices of small caps, while large caps are not sensitive to market illiquidity shocks. This differs slightly from results based on U.S. data where illiquidity shocks reduce all stock prices but most notably those of small caps. Robustness tests validate our findings. Our results are consistent with previous studies which reported that the “zero-return” ratio predicts significantly the return-illiquidity relationship on emerging markets.

scholarly journals Challenges in Timeseries Analysis from Microlensing

2016 ◽  
Vol 12 (S325) ◽  
pp. 253-258
Author(s):  
R. A. Street
Keyword(s):  
Data Analysis ◽  
Software Development ◽  
Formation Mechanisms ◽  
Wide Field ◽  
Detection Techniques ◽  
Timeseries Analysis ◽  
Modeling Software ◽  
New Software Development ◽  
Host Stars ◽  
Infrared Survey

AbstractDespite a flood of discoveries over the last ~ 20 years, our knowledge of the exoplanet population is incomplete owing to a gap between the sensitivities of different detection techniques. However, a census of exoplanets at all separations from their host stars is essential to fully understand planet formation mechanisms. Microlensing offers an effective way to bridge the gap around 1–10 AU and is therefore one of the major science goals of the Wide Field Infrared Survey Telescope (WFIRST) mission. WFIRST’s survey of the Galactic Bulge is expected to discover ~ 20,000 microlensing events, including ~ 3000 planets, which represents a substantial data analysis challenge with the modeling software currently available. This paper highlights areas where further work is needed. The community is encouraged to join new software development efforts aimed at making the modeling of microlensing events both more accessible and rigorous.

scholarly journals Associations between public health team engagement in local alcohol licensing and public health and crime outcomes in England and Scotland: a timeseries analysis

The Lancet ◽  
2021 ◽  
Vol 398 ◽  
pp. S40
Author(s):  
Frank de Vocht ◽  
Cheryl McQuire ◽  
Claire Ferraro ◽  
Philippa Williams ◽  
Madeleine Henney ◽  
...  
Keyword(s):  
Public Health ◽  
Health Team ◽  
Timeseries Analysis ◽  
Team Engagement

scholarly journals Human Development and Economic Uncertainties: Exploring Another Dimension of Development

2013 ◽  
Vol 18 (Special Edition) ◽  
pp. 305-334 ◽  
Author(s):  
Jamshed Y. Uppal ◽  
Syeda Rabab Mudakkar
Keyword(s):  
Social Welfare ◽  
Human Development ◽  
Human Development Index ◽  
Time Variability ◽  
The Political ◽  
Human Capabilities ◽  
Additional Information ◽  
Timeseries Analysis ◽  
Percentage Loss ◽  
Income Component

This study makes the case that economic uncertainties—i.e., the extent to which economies face systemic uncertainties—need to be considered another dimension of human development because they render development vulnerable, diminish social welfare, and constrain human capabilities. We propose a methodology for adjusting the human development index (HDI) for economic uncertainties, using the time variability of income changes as a proxy. We construct an adjusted index associated with the income component for the 2011 HDI. Our analysis indicates that such an index contains additional information. The percentage loss in the income component of the HDI seems to reflect the variability in economic indicators arising from the political and economic tribulations experienced by each country. In Pakistan’s case, the results of a timeseries analysis of the percentage loss from the uncertainty adjustment appear to closely trace the country’s political and economic upheavals.

An innovative, timeseries-analysis-based method to extract rainfall-streamflow events from continuous timeseries

2021 ◽  
Author(s):  
Giulia Giani ◽  
Miguel Angel Rico-Ramirez ◽  
Ross Woods
Keyword(s):  
Ad Hoc ◽  
Traditional Approach ◽  
Moving Average ◽  
Hydrograph Separation ◽  
Minimum Duration ◽  
Timeseries Analysis ◽  
Cross Correlation Analysis ◽  
Wide Range ◽  
Dry Spell ◽  
Peak Over Threshold

<p>A widely accepted objective methodology to select individual rainfall-streamflow events is missing and this makes it difficult to synthesize findings from independent research initiatives. In fact, the selection of individual events is a fundamental step in many hydrological studies, but the importance and impact of the choices made at this stage are largely unrecognised.</p><p>The event selection methods found in the literature start by looking at either the rainfall timeseries or the streamflow timeseries. Moreover, most of the methodologies involve hydrograph separation, which is a highly uncertain step and can be performed using many different algorithms. Further increasing the subjectivity of the procedure, a wide range of ad hoc conditions are usually applied (e.g. peak-over-threshold, minimum duration of rainfall event, minimum duration of dry spell, minimum rainfall intensity…).</p><p>For these reasons, we present a new methodology to extract rainfall-streamflow events which minimizes the conceptual hypotheses and user’s choices, and bases the identification of the events mainly on the joint fluctuations of the two signals. The proposed methodology builds upon a timeseries analysis technique to estimate catchment response time, the Detrending Moving-average Cross-correlation Analysis-based method.</p><p>The proposed method has the advantage of looking simultaneously at the evolution in time of rainfall and streamflow timeseries, providing a more systemic detection of events. Moreover, the presented method can easily be adapted to extract events at different time resolutions (provided the resolution is fine enough to capture the delay between the rainfall and streamflow responses).</p><p>Properties of the events extracted with the proposed method are compared with the ones of the events extracted with the most traditional approach (based on hydrograph separation) to show strengths and weaknesses of the two techniques and suggest in which situations the proposed method can be most useful.</p>

A practical, objective, robust technique to directly estimate time of concentration

2020 ◽  
Author(s):  
Giulia Giani ◽  
Miguel Angel Rico-Ramirez ◽  
Ross Woods
Keyword(s):  
Concentration Distribution ◽  
Discharge Rate ◽  
Computational Cost ◽  
Reliable Estimate ◽  
Hydrograph Separation ◽  
Time Of Concentration ◽  
Modelling Uncertainty ◽  
Timeseries Analysis ◽  
Analysis Technique ◽  
The Uk

<p>Time of concentration is one of the key time variables in hydrology and it is essential for hydrograph design and hydrological modelling. Uncertainty in its estimation can cause errors in peak discharge rate and timing of flood events.</p><p>A unique recognized definition and methodology for its estimate is lacking and the multiple definitions and estimation procedures available in literature can give numerical prediction which can differ by up to 500% (Grimaldi et al., 2012). This result is not surprising given the high subjectivity of the traditionally used method to directly estimate time of concentration, also used for the calibration of the widely applied empirical formulae.</p><p>Given the importance of this time parameter in hydrology and the lack of a recognized and easily reproducible procedure for its estimate, here we propose a practical, objective, robust methodology to directly estimate time of concentration from rainfall and streamflow observations only. It’s a timeseries analysis technique used already in the Economics field (Kristoufek, 2014), that have been adapted to estimate time of concentration.</p><p>Compared to the traditionally used method, which is event based and requires hyetograph and hydrograph separation, the proposed methodology is designed to find the time delay from the original continuous timeseries but can also be applied to individual events by creating a timeseries of copies of the same event.</p><p>In the first place, the median of time of concentration distribution with the proposed methodology has been evaluated against the one with the traditionally used one in 79 catchments across the UK, showing that in most of the sites estimates coming from the two methods are very similar (correlation value of 0.82). This means that it is possible to avoid the separation of the hydrograph, required by the traditionally used method, which is a highly subjective procedure.</p><p>Secondly, we show that, when considering the proposed methodology only, for each catchment the time of concentration estimate using the continuous timeseries has a small discrepancy compared to the median of the time of concentration distribution of the single events estimates (correlation value of 0.94). Therefore, rainfall-streamflow events selection is not necessary and a reliable estimate of time of concentration can be obtained by applying the proposed methodology on the continuous timeseries at once, reducing the computational cost.</p><p>The proposed timeseries analysis technique is easy to automate, reproducible and make possible to objectively compare time of concentration estimates in all the catchments where the resolution of rainfall and streamflow timeseries is high enough to capture the runoff process.</p>

Uninformed Trading and Information Uncertainty in the Post-IPO Market

2015 ◽  
Vol 05 (01) ◽  
pp. 1550016
Author(s):  
Rahul Ravi
Keyword(s):  
Adverse Selection ◽  
Initial Public Offering ◽  
Strong Evidence ◽  
Negative Relationship ◽  
Secondary Market ◽  
Selection Problem ◽  
Information Uncertainty ◽  
Timeseries Analysis ◽  
Public Offering ◽  
Adverse Selection Problem

Underpricing in the initial public offering (IPO) market has been traditionally explained as a means of attracting liquidity traders. We find strong evidence suggesting that the intensity of trading coming from these uninformed traders and the rate of idiosyncratic information arrival play important roles in determining the level of adverse selection cost of trading in the post-IPO market. Timeseries analysis reveals that this cost is lowest immediately post-IPO and it increases monotonically in the first 8–12 weeks of secondary market trading. Order flow variability and the fraction of small trades (both proxies for the extent of uninformed trading) are at their highest in the immediate aftermarket and their levels decay for the next 8–12 weeks. Our results allude to the existence of a negative relationship between underpricing and the adverse selection problem in the post-IPO market, mediated by the intensity of uninformed trading.

scholarly journals SEASONAL STOCHASTIC SIMULATION AND TIMESERIES MODELLING - ANALYSIS OF A KARSTIC SPRING IN CENTRAL MACEDONIA, GREECE

2017 ◽  
Vol 50 (2) ◽  
pp. 808
Author(s):  
I. Lappas ◽  
M. Lazaridou
Keyword(s):  
Moving Average ◽  
Demand Management ◽  
Information Criterion ◽  
Spring Discharge ◽  
Discharge Data ◽  
The North ◽  
Timeseries Analysis ◽  
Karstic Spring ◽  
Monthly Discharge ◽  
One Year

The objective of this paper is to find an appropriate Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model for fitting the monthly discharge of a karstic spring located at the North of the city of Serres (Agios Ioannis, Mount Menikio) by considering the minimum of Akaike Information Criterion (AIC). Box- Jenkins methodology applies models to find the best fit of a timeseries to past values of this timeseries, in order to make forecasting and consists of a four-step iterative procedure: identification, estimation, diagnostic check and forecasting. Timeseries analysis and forecasting of hydrological parameters such as spring discharge may be useful in decision making and optimum water resources usage. In this study, monthly discharge measurements are analysed. Initial data are firstly transformed to normal and stationary using differencing methods. Autocorrelation and Partial Autocorrelation functions are calculated to determine the order of Autoregressive and Moving Average parameters and residuals are then checked to show the “white noise”. The spring discharge data are forecasted based on the selected model up to 2008 and are then compared with measured values. The timeseries model SARIMA (2,1,1)(1,0,1)12 could be used in monthly discharge forecasting at a short time (upcoming one year) with a simple and explicit model structure in order to help decision m akers to establish priorities in terms of water demand management. Finally, the corr elation coefficient between the observed and fitted data is essentially high, while the absolute and relative errors are significantly low.

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