Earthquake prediction from high frequency groundwater level data: A case study from Gujarat, India

Carsharing as a service has been growing rapidly worldwide. Its expansion has drawn wide attention in the research community with regard to the underlying driving factors and user characteristics. Despite these extensive investigations, there are still limited studies focusing on the examination of users using carsharing as a commuting mode. The answers to questions such as what kind of people would like to use carsharing for commuting and why they frequently use carsharing to commute are not clear. To enrich our understanding of these problems, this paper aims to investigate carsharing commuters in a mega city. Specifically, it intends to integrate the actual user order data with survey data from 1,920 participants to uncover the characteristics of carsharing commuters. Data from the Evcard carsharing systems in Shanghai were explicitly analyzed. Through descriptive analysis and logistic regression models, the characteristics and critical factors that affect the choice of carsharing as a commuting mode were captured. The results show that: 1. carsharing commuters mostly live or work in suburban areas in which public transport accessibility is limited; 2. carsharing commuters are more likely to be highly educated, in a higher income bracket, and older than other carsharing members; 3. high-frequency carsharing commuters own a reduced number of private cars; and 4. those high-frequency carsharing commuters with higher income are less sensitive to the carsharing costs caused by congestion. The findings in the study offer some insights into carsharing commuters and provide some supportive information for considering policies in developing carsharing systems in urban areas.

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HFTViz: Visualization for the exploration of high frequency trading data

Information Visualization ◽

10.1177/14738716211064921 ◽

2021 ◽

pp. 147387162110649

Author(s):

Javad Yaali ◽

Vincent Grégoire ◽

Thomas Hurtut

Keyword(s):

Portfolio Management ◽

High Frequency ◽

High Speed ◽

High Frequency Trading ◽

Domain Experts ◽

Risk Return ◽

Visualization Tools ◽

Liquidity Measures ◽

Trading Volumes

High Frequency Trading (HFT), mainly based on high speed infrastructure, is a significant element of the trading industry. However, trading machines generate enormous quantities of trading messages that are difficult to explore for financial researchers and traders. Visualization tools of financial data usually focus on portfolio management and the analysis of the relationships between risk and return. Beside risk-return relationship, there are other aspects that attract financial researchers like liquidity and moments of flash crashes in the market. HFT researchers can extract these aspects from HFT data since it shows every detail of the market movement. In this paper, we present HFTViz, a visualization tool designed to help financial researchers explore the HFT dataset provided by NASDAQ exchange. HFTViz provides a comprehensive dashboard aimed at facilitate HFT data exploration. HFTViz contains two sections. It first proposes an overview of the market on a specific date. After selecting desired stocks from overview visualization to investigate in detail, HFTViz also provides a detailed view of the trading messages, the trading volumes and the liquidity measures. In a case study gathering five domain experts, we illustrate the usefulness of HFTViz.

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Composition of high frequency ambient noise from cross-correlation: A case study using a small aperture array

Earthquake Science ◽

10.1007/s11589-010-0742-6 ◽

2010 ◽

Vol 23 (5) ◽

pp. 433-438 ◽

Cited By ~ 2

Author(s):

Weitao Wang ◽

Sidao Ni ◽

Baoshan Wang

Keyword(s):

High Frequency ◽

Ambient Noise ◽

Cross Correlation ◽

Small Aperture ◽

Aperture Array

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A novel deseasonalized time series model with an improved seasonal estimate for groundwater level predictions

H2Open Journal ◽

10.2166/h2oj.2019.022 ◽

2019 ◽

Vol 2 (1) ◽

pp. 25-44 ◽

Cited By ~ 2

Author(s):

S. Mohanasundaram ◽

G. Suresh Kumar ◽

Balaji Narasimhan

Keyword(s):

Time Series ◽

Groundwater Level ◽

Moving Average ◽

Seasonal Component ◽

Univariate Time Series ◽

Level Data ◽

Proposed Model ◽

Interval Approach ◽

Prediction And Forecasting ◽

Observation Wells

Abstract Groundwater level prediction and forecasting using univariate time series models are useful for effective groundwater management under data limiting conditions. The seasonal autoregressive integrated moving average (SARIMA) models are widely used for modeling groundwater level data as the groundwater level signals possess the seasonality pattern. Alternatively, deseasonalized autoregressive and moving average models (Ds-ARMA) can be modeled with deseasonalized groundwater level signals in which the seasonal component is estimated and removed from the raw groundwater level signals. The seasonal component is traditionally estimated by calculating long-term averaging values of the corresponding months in the year. This traditional way of estimating seasonal component may not be appropriate for non-stationary groundwater level signals. Thus, in this study, an improved way of estimating the seasonal component by adopting a 13-month moving average trend and corresponding confidence interval approach has been attempted. To test the proposed approach, two representative observation wells from Adyar basin, India were modeled by both traditional and proposed methods. It was observed from this study that the proposed model prediction performance was better than the traditional model's performance with R2 values of 0.82 and 0.93 for the corresponding wells' groundwater level data.

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Multivariate and Online Prediction of Closing Price Using Kernel Adaptive Filtering

Computational Intelligence and Neuroscience ◽

10.1155/2021/6400045 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Shambhavi Mishra ◽

Tanveer Ahmed ◽

Vipul Mishra ◽

Manjit Kaur ◽

Thomas Martinetz ◽

...

Keyword(s):

Adaptive Filtering ◽

Stock Prices ◽

High Frequency ◽

Time Windows ◽

Stock Index ◽

Level Data ◽

Traditional Classification ◽

Regression Problems ◽

The Common ◽

Online Prediction

This paper proposes a multivariate and online prediction of stock prices via the paradigm of kernel adaptive filtering (KAF). The prediction of stock prices in traditional classification and regression problems needs independent and batch-oriented nature of training. In this article, we challenge this existing notion of the literature and propose an online kernel adaptive filtering-based approach to predict stock prices. We experiment with ten different KAF algorithms to analyze stocks’ performance and show the efficacy of the work presented here. In addition to this, and in contrast to the current literature, we look at granular level data. The experiments are performed with quotes gathered at the window of one minute, five minutes, ten minutes, fifteen minutes, twenty minutes, thirty minutes, one hour, and one day. These time windows represent some of the common windows frequently used by traders. The proposed framework is tested on 50 different stocks making up the Indian stock index: Nifty-50. The experimental results show that online learning and KAF is not only a good option, but practically speaking, they can be deployed in high-frequency trading as well.

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Simple compared to covariate-constrained randomization methods in balancing baseline characteristics: a case study of randomly allocating 72 hemodialysis centers in a cluster trial

10.21203/rs.3.rs-278523/v1 ◽

2021 ◽

Author(s):

Ahmed A Al-Jaishi ◽

Stephanie N Dixon ◽

Eric McArthur ◽

PJ Devereaux ◽

Lehana Thabane ◽

...

Keyword(s):

Principal Components ◽

Cluster Randomized Trial ◽

Median Number ◽

P Value ◽

Individual Level ◽

Level Data ◽

Parallel Group ◽

Baseline Characteristics ◽

Cluster Randomized

Abstract Background and aim: Some parallel-group cluster randomized trials use covariate-constrained rather than simple randomization. This is done to increase the chance of balancing the groups on cluster- and patient-level baseline characteristics. This study assessed how well two covariate-constrained randomization methods balanced baseline characteristics compared with simple randomization. Methods: We conducted a mock three-year cluster randomized trial, with no active intervention, that started April 1st, 2014, and ended March 31st, 2017. We included a total of 11,832 patients from 72 hemodialysis centers (clusters) in Ontario, Canada. We randomly allocated the 72 clusters into two groups in a 1:1 ratio on a single date using individual- and cluster-level data available up to April 1st, 2013. Initially, we generated 1,000 allocation schemes using simple randomization. Then, as an alternative, we performed covariate-constrained randomization based on historical data from these centers. In one analysis, we restricted on a set of 11 individual-level prognostic variables; in the other, we restricted on principal components generated using 29 baseline historical variables. We created 300,000 different allocations for the covariate-constrained randomizations, and we restricted our analysis to the 30,000 best allocations. We then randomly sampled 1,000 schemes from the 30,000 best allocations. We summarized our results with each randomization approach as the median (25th, 75th percentile) number of balanced baseline characteristics. There were 156 baseline characteristics, and a variable was balanced when the between-group standardized difference was ≤ 10%. Results: The three randomization techniques had at least 125 of 156 balanced baseline characteristics in 90% of sampled allocations. The median number of balanced baseline characteristics using simple randomization was 147 (142, 150). The corresponding value for covariate-constrained randomization using 11 prognostic characteristics was 149 (146, 151), while for principal components, the value was 150 (147, 151). The median number of balanced baseline characteristics using the two covariate-constrained randomizations were statistically different from simple randomization (p-value < 0.0001). Conclusion: In this setting with 72 clusters, constraining the randomization using historical information achieved better balance on baseline characteristics compared with simple randomization; however, the magnitude of benefit was modest.

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Critical Land Detection using Remote Sensing Device and Geographic Information (Case Study of Critical Land Basic of Catchments Area, Alang, Wonogiri)

Forum Geografi ◽

10.23917/forgeo.v19i1.4567 ◽

2017 ◽

Vol 19 (1) ◽

pp. 1

Author(s):

Beny Harjadi

Keyword(s):

Remote Sensing ◽

Field Survey ◽

Critical Level ◽

Satellite Image ◽

Geographic Information ◽

Level Data ◽

The Difference

Work criteria and indicator of Catchments Area need to be determined because the success and the failure of cultivating Catchments Area can be monitored and evaluated through the determined criteria. Criteria Indicators in utilizing land, one of them is determined based on the erosion index and the ability of utilizing land, for analyzing the land critical level. However, the determination of identification and classification of land critical level has not been determined; as a result the measurement of how wide the real critical land is always changed all the year. In this study, it will be tried a formula to determine the land critical/eve/ with various criteria such as: Class KPL (Ability of Utilizing Land) and the difference of the erosion tolerance value with the great of the erosion compared with land critical level analysis using remote sensing devices. The aim of studying land critical level detection using remote sensing tool and Geographic Information System (SIG) are:1. The backwards and the advantages of critical and analysis method2. Remote Sensing Method for critical and classification3. Critical/and surveyed method in the field (SIG) Collecting and analyzing data can be found from the field survey and interpretation of satellite image visually and using computer. The collected data are analyzed as:a. Comparing the efficiency level and affectivity of collecting biophysical data through field survey, sky photo interpretation, and satellite image analysis.b. Comparing the efficiency level and affectivity of land critical level data that are found from the result of KPL with the result of the measurement of the erosion difference and erosion tolerance.

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