Do emissions and income have a common trend? A country-specific, time-series, global analysis, 1970–2008

Savaris et al. (2021) aim at "verifying if staying at home had an impact on mortality rates." This short note shows that the methodology they have applied in their paper does not allow them to do so. An estimated coefficient β≈0 does not imply that there is no association between the variables in either country. Rather, their pairwise difference regressions are computing coefficients that are weighted-averages of region-specific time series regressions, such that it is possible that the association is significant in both regions but their weighted-averages is close to zero. Therefore, the results do not back up the conclusions of the paper.

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An Efficient Time Series Forecasting Method Exploiting Fuzziness and Turbulences in Data

Intelligent Systems ◽

10.4018/978-1-5225-5643-5.ch078 ◽

2018 ◽

pp. 1773-1791 ◽

Cited By ~ 1

Author(s):

Prateek Pandey ◽

Shishir Kumar ◽

Sandeep Shrivastava

Keyword(s):

Time Series ◽

Performance Comparison ◽

Time Series Forecasting ◽

Fuzzy Time Series ◽

Data Set ◽

Common Trend ◽

Proposed Model ◽

Forecasting Method ◽

High Degree ◽

Degree Of Instability

In recent years, there has been a growing interest in Time Series forecasting. A number of time series forecasting methods have been proposed by various researchers. However, a common trend found in these methods is that they all underperform on a data set that exhibit uneven ups and downs (turbulences). In this paper, a new method based on fuzzy time-series (henceforth FTS) to forecast on the fundament of turbulences in the data set is proposed. The results show that the turbulence based fuzzy time series forecasting is effective, especially, when the available data indicate a high degree of instability. A few benchmark FTS methods are identified from the literature, their limitations and gaps are discussed and it is observed that the proposed method successfully overcome their deficiencies to produce better results. In order to validate the proposed model, a performance comparison with various conventional time series models is also presented.

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Estimation and classification of temporal trends to support integrated ecosystem assessment

ICES Journal of Marine Science ◽

10.1093/icesjms/fsaa111 ◽

2020 ◽

Author(s):

Hiroko Kato Solvang ◽

Benjamin Planque

Keyword(s):

Time Series ◽

Barents Sea ◽

Multivariate Time Series ◽

Marine Ecosystem ◽

Temporal Trends ◽

Dynamic Factor ◽

Short Time Series ◽

Common Trends ◽

Common Trend ◽

Short Time

Abstract We propose a trend estimation and classification (TREC) approach to estimating dominant common trends among multivariate time series observations. Our methods are based on two statistical procedures that includes trend modelling and discriminant analysis for classifying similar trend (common trend) classes. We use simulations to evaluate the proposed approach and compare it with a relevant dynamic factor analysis in the time domain, which was recently proposed to estimate common trends in fisheries time series. We apply the TREC approach to the multivariate short time series datasets investigated by the ICES integrated assessment working groups for the Norwegian Sea and the Barents Sea. The proposed approach is robust for application to short time series, and it directly identifies and classifies the dominant trends underlying observations. Based on the classified trend classes, we suggest that communication among stakeholders like marine managers, industry representatives, non-governmental organizations, and governmental agencies can be enhanced by finding the common tendency between a biological community in a marine ecosystem and the environmental factors, as well as by the icons produced by generalizing common trend patterns.

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Using barometric time series of the IMS infrasound network for a global analysis of thermally induced atmospheric tides

Atmospheric Measurement Techniques ◽

10.5194/amt-11-2027-2018 ◽

2018 ◽

Vol 11 (4) ◽

pp. 2027-2040 ◽

Cited By ~ 1

Author(s):

Patrick Hupe ◽

Lars Ceranna ◽

Christoph Pilger

Keyword(s):

Time Series ◽

Global Analysis ◽

Atmospheric Dynamics ◽

Air Pressure ◽

Atmospheric Tides ◽

High Temporal Resolution ◽

Data Sets ◽

Atmospheric Conditions ◽

Thermally Induced ◽

Tidal Harmonics

Abstract. The International Monitoring System (IMS) has been established to monitor compliance with the Comprehensive Nuclear-Test-Ban Treaty and comprises four technologies, one of which is infrasound. When fully established, the IMS infrasound network consists of 60 sites uniformly distributed around the globe. Besides its primary purpose of determining explosions in the atmosphere, the recorded data reveal information on other anthropogenic and natural infrasound sources. Furthermore, the almost continuous multi-year recordings of differential and absolute air pressure allow for analysing the atmospheric conditions. In this paper, spectral analysis tools are applied to derive atmospheric dynamics from barometric time series. Based on the solar atmospheric tides, a methodology for performing geographic and temporal variability analyses is presented, which is supposed to serve for upcoming studies related to atmospheric dynamics. The surplus value of using the IMS infrasound network data for such purposes is demonstrated by comparing the findings on the thermal tides with previous studies and the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2), which represents the solar tides well in its surface pressure fields. Absolute air pressure recordings reveal geographical characteristics of atmospheric tides related to the solar day and even to the lunar day. We therefore claim the chosen methodology of using the IMS infrasound network to be applicable for global and temporal studies on specific atmospheric dynamics. Given the accuracy and high temporal resolution of the barometric data from the IMS infrasound network, interactions with gravity waves and planetary waves can be examined in future for refining the knowledge of atmospheric dynamics, e.g. the origin of tidal harmonics up to 9 cycles per day as found in the barometric data sets. Data assimilation in empirical models of solar tides would be a valuable application of the IMS infrasound data.

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Obtaining crop-specific time profiles of NDVI: the use of unmixing approaches for serving the continuity between SPOT-VGT and PROBA-V time series

International Journal of Remote Sensing ◽

10.1080/01431161.2014.883106 ◽

2014 ◽

Vol 35 (7) ◽

pp. 2615-2638 ◽

Cited By ~ 14

Author(s):

C. Atzberger ◽

A.R. Formaggio ◽

Y.E. Shimabukuro ◽

T. Udelhoven ◽

M. Mattiuzzi ◽

...

Keyword(s):

Time Series ◽

Specific Time ◽

Time Profiles

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EFFICIENT MONITORING OF TIME SERIES DATA USING DYNAMIC ALERTING

10.35543/osf.io/p2xcw ◽

2019 ◽

Author(s):

Girish L

Keyword(s):

Time Series ◽

Data Centers ◽

Time Series Data ◽

Series Data ◽

Time Interval ◽

Specific Time ◽

Specific Data ◽

Cloud Data ◽

Data Collector ◽

Cloud Data Centers

Network and Cloud Data Centers generate a lot of data every second, this data can be collected as a time series data. A time series is a sequence taken at successive equally spaced points in time, that means at a particular time interval to a specific time, the values of specific data that was taken is known as a data of a time series. This time series data can be collected using system metrics like CPU, Memory, and Disk utilization. The TICK Stack is an acronym for a platform of open source tools built to make collection, storage, graphing, and alerting on time series data incredibly easy. As a data collector, the authors are using both Telegraf and Collectd, for storing and analyzing data and the time series database InfluxDB. For plotting and visualizing, they use Chronograf along with Grafana. Kapacitor is used for alert refinement and once system metrics usage exceeds the specified threshold, the alert is generated and sends it to the system admin.

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Global & Geographical Mapping and Visualization Method for Personal/Collective Health Data with 5D World Map System

Frontiers in Artificial Intelligence and Applications - Information Modelling and Knowledge Bases XXXII ◽

10.3233/faia200825 ◽

2020 ◽

Author(s):

Shiori Sasaki ◽

Koji Murakami ◽

Yasushi Kiyoki ◽

Asako Uraki

Keyword(s):

Time Series ◽

Health Monitoring ◽

Potential Risk ◽

Global Analysis ◽

Collaborative Work ◽

Health Data ◽

Multimedia Data ◽

Personal Health ◽

Single View ◽

Semantic Correlation

This paper presents a new knowledge base creation method for personal/collective health data with knowledge of preemptive care and potential risk inspection with a global and geographical mapping and visualization functions of 5D World Map System. The final goal of this research project is a realization of a system to analyze the personal health/bio data and potential-risk inspection data and provide a set of appropriate coping strategies and alert with semantic computing technologies. The main feature of 5D World Map System is to provide a platform of collaborative work for users to perform a global analysis for sensing data in a physical space along with the related multimedia data in a cyber space, on a single view of time-series maps based on the spatiotemporal and semantic correlation calculations. In this application, the concrete target data for world-wide evaluation is (1) multi-parameter personal health/bio data such as blood pressure, blood glucose, BMI, uric acid level etc. and daily habit data such as food, smoking, drinking etc., for a health monitoring and (2) time-series multi-parameter collective health/bio data in the national/regional level for global analysis of potential cause of disease. This application realizes a new multidimensional data analysis and knowledge sharing for both a personal and global level health monitoring and disease analysis. The results are able to be analyzed by the time-series difference of the value of each spot, the differences between the values of multiple places in a focused area, and the time-series differences between the values of multiple locations to detect and predict a potential-risk of diseases.

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