In the year 2010 a continual radon measurement was established at Mladeč Caves in the Czech Republic using a continual radon monitor RADIM3A. In order to model radon time series in the years 2010-15, the Box-Jenkins Methodology, often used in econometrics, was applied. Because of the behavior of radon concentrations (RCs), a seasonal integrated, autoregressive moving averages model with exogenous variables (SARIMAX) has been chosen to model the measured time series. This model uses the time series seasonality, previously acquired values and delayed atmospheric parameters, to forecast RC. The developed model for RC time series is called regARIMA(5,1,3). Model residuals could be retrospectively compared with seismic evidence of local or global earthquakes, which occurred during the RCs measurement. This technique enables us to asses if continuously measured RC could serve an earthquake precursor.
- MeSH
- Interrupted Time Series Analysis MeSH
- Caves MeSH
- Radiation Monitoring methods MeSH
- Predictive Value of Tests MeSH
- Soil Pollutants, Radioactive analysis MeSH
- Radon analysis MeSH
- Seasons MeSH
- Earthquakes * MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Czech Republic MeSH
... 11 -- 2.3 Multiple Periodicities 17 -- 2.4 Orthogonality of Sinusoids 19 -- 2.5 Effect of Discrete Time ... ... -- 7.6 Complex Time Series -- 7.7 Sunspots: The Complex Series Appendix -- 8 The Spectrum -- 8.1 Periodogram ... ... Analysis of Wheat Prices -- 8.2 Analysis of Segments of a Series -- CONTENTS Xiii -- 8.3 Smoothing the ... ... Series Theory 167 -- 9.1 Stationary Time Series 167 -- 9.2 Continuous Spectra 173 -- 9.3 Time Averaging ... ... Domain Analysis 233 -- 11.2 Spatial Series 234 -- 11.3 Multiple Series 236 -- 11.4 Higher Order Spectra ...
Wiley series in probability and statistics
2nd ed. xiv, 261 s. : il.
OBJECTIVES: Imposing taxes on unhealthy goods can generate income, raise people's health awareness, and eventually decrease the prevalence of chronic diseases. Our aim was to assess the impact of Hungary's public health product tax (PHPT) since its implementation in September 2011. Differences in purchasing habits between households with different income statuses were also compared. METHODS: A retrospective, descriptive analysis of tax bases and income was carried out, and an interrupted time series analysis using the generalised least squares method was performed to examine the changes in trends regarding the purchase of taxable products before and after the implementation of the tax. The amount of tax base (in kilograms or litres), income (in HUF and EUR), and annual purchased quantity of food and beverage groups per household were assessed. Data were derived from the National Tax and Customs Administration of Hungary and the Hungarian Household Budget and Living Conditions Surveys. The study sample was composed of households who participated in the surveys (mean = 8,359, SD = 1,146) between 2006 and 2018. RESULTS: The households' tax bases and incomes increased constantly (with a few exceptions). The total revenue was 19.49 billion HUF (67.37 million EUR) in 2012 and 59.19 billion HUF (168.55 million EUR) in 2020. However, the households' purchasing habits did not change as expected. A significant short-term decrease (between 2012 and 2013) in purchasing unhealthy goods was observed for three groups: soft drinks (p = 0.009), jams (p = 0.047), and fruit juices (p = 0.038). Only soft drinks showed a significant decreasing trend in the post-intervention period between 2012 and 2018 (p < 0.001). CONCLUSIONS: We concluded that the PHPT did not decrease households' unhealthy food purchasing trend, although it has a positive effect as it can create revenue for health care and health-promoting programmes.
- MeSH
- Interrupted Time Series Analysis MeSH
- Taxes * MeSH
- Humans MeSH
- Beverages MeSH
- Commerce MeSH
- Retrospective Studies MeSH
- Public Health * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Hungary MeSH
Anomalies in the radon (222Rn) releases in underground environments are one of the phenomena that can be observed before earthquake occurrence. Continuous measurements of radon activity concentration, and of meteorological parameters that influence the gas emission, were performed in three Slovak and Czech caves during 1-y period (1 July 2016-30 June 2017). The radon activity concentration in caves shows seasonal variations, with maxima reached during summer months. The anomalies in the radon time series are identified using a combination of three mathematical methods: multiple linear regression, empirical mode decomposition and support vector regression. The radon anomaly periods were compared with earthquake occurrences in Europe. Coincidences between both phenomena were found, since all monitored caves reflect contemporaneous local tectonic changes. The results indicate that radon continuous monitoring could assist a better understanding of radon emissions, along active tectonic structures, during seismic events.
- MeSH
- Interrupted Time Series Analysis MeSH
- Caves * MeSH
- Humans MeSH
- Radiation Monitoring methods MeSH
- Gases analysis MeSH
- Soil Pollutants, Radioactive analysis MeSH
- Radon analysis MeSH
- Seasons MeSH
- Earthquakes statistics & numerical data MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Czech Republic MeSH
- Slovakia MeSH
In this comparative study, six causality detection methods were compared, namely, the Granger vector autoregressive test, the extended Granger test, the kernel version of the Granger test, the conditional mutual information (transfer entropy), the evaluation of cross mappings between state spaces, and an assessment of predictability improvement due to the use of mixed predictions. Seven test data sets were analyzed: linear coupling of autoregressive models, a unidirectional connection of two Hénon systems, a unidirectional connection of chaotic systems of Rössler and Lorenz type and of two different Rössler systems, an example of bidirectionally connected two-species systems, a fishery model as an example of two correlated observables without a causal relationship, and an example of mediated causality. We tested not only 20000 points long clean time series but also noisy and short variants of the data. The standard and the extended Granger tests worked only for the autoregressive models. The remaining methods were more successful with the more complex test examples, although they differed considerably in their capability to reveal the presence and the direction of coupling and to distinguish causality from mere correlation.
- MeSH
- Time Factors MeSH
- Causality MeSH
- Systems Analysis MeSH
- Models, Theoretical * MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
- Comparative Study MeSH
