scholarly journals Long-Term Linear Trends in Consumer Price Indices

2008 ◽  
Author(s):  
Ivan Kitov ◽  
Oleg Kitov
Keyword(s):  
Price Indices ◽  
Term Linear ◽  
Linear Trends

scholarly journals Long-term linear trends mask phenological shifts

2016 ◽  
Vol 60 (11) ◽  
pp. 1611-1613 ◽  
Author(s):  
Yongshuo H Fu ◽  
Shilong Piao ◽  
Philippe Ciais ◽  
Mengtian Huang ◽  
Annette Menzel ◽  
...  
Keyword(s):  
Term Linear ◽  
Phenological Shifts ◽  
Linear Trends

scholarly journals Global and long-term comparison of SCIAMACHY limb ozone profiles with correlative satellite data (2002–2008)

2011 ◽  
Vol 4 (4) ◽  
pp. 4867-4910
Author(s):  
S. Mieruch ◽  
M. Weber ◽  
C. von Savigny ◽  
A. Rozanov ◽  
H. Bovensmann ◽  
...  
Keyword(s):  
Satellite Data ◽  
Convective Cloud ◽  
Residual Effects ◽  
Statistical Hypothesis ◽  
Systematic Bias ◽  
Statistical Hypothesis Testing ◽  
Trend Model ◽  
The Tropics ◽  
Linear Trends

Abstract. SCIAMACHY limb scatter ozone profiles from 2002 to 2008 have been compared with MLS (2005–2008), SABER (2002–2008), SAGE II (2002–2005), HALOE (2002–2005) and ACE-FTS (2004–2008) measurements. The comparison is performed for global zonal averages and heights from 10 to 50 km in one km steps. The validation was performed by comparing monthly mean zonal means and by comparing averages over collocated profiles within a zonal band and month. Both approaches yield similar results. For most of the stratosphere SCIAMACHY agrees to within 10 % or better with other correlative data. A systematic bias of SCIAMACHY ozone of up to 100 % between 10 and 20 km in the tropics points to some remaining issues with regard to convective cloud interference. Statistical hypothesis testing reveals at which altitudes and in which region differences between SCIAMACHY and other satellite data are statistically significant. We also estimated linear trends from monthly mean data for different periods where SCIAMACHY has common observations with other satellite data using a classical trend model with QBO and seasonal terms in order to draw conclusions on potential instrumental drifts as a function of latitude and altitude. SCIAMACHY exhibits a statistically significant negative trend in the range of of about 1–3 % per year depending on latitude during the period 2002–2005 (overlapping with HALOE and SAGE II) and somewhat less during 2002–2008 (overlapping with SABER) in the altitude range of 30–40 km, while in the period 2004–2008 (overlapping with MLS and ACE-FTS) no significant trends are observed. The statistically significant negative trends only observed with SCIAMACHY data point at some residual effects from errors in the tangent height registration.

scholarly journals Early childhood diarrhoea and helminthiases associate with long-term linear growth faltering

2001 ◽  
Vol 30 (6) ◽  
pp. 1457-1464 ◽  
Author(s):  
SR Moore ◽  
AAM Lima ◽  
MR Conaway ◽  
JB Schorling ◽  
AM Soares ◽  
...  
Keyword(s):  
Early Childhood ◽  
Linear Growth ◽  
Childhood Diarrhoea ◽  
Term Linear ◽  
Growth Faltering

scholarly journals Comparison of GPS tropospheric delays derived from two consecutive EPN reprocessing campaigns from the point of view of climate monitoring

2016 ◽  
Vol 9 (9) ◽  
pp. 4861-4877 ◽  
Author(s):  
Zofia Baldysz ◽  
Grzegorz Nykiel ◽  
Andrzej Araszkiewicz ◽  
Mariusz Figurski ◽  
Karolina Szafranek
Keyword(s):  
Time Series ◽  
Point Of View ◽  
Trend Test ◽  
Data Sets ◽  
Total Delay ◽  
University Of Technology ◽  
Time Period ◽  
Seasonal Signals ◽  
Linear Trends

Abstract. The main purpose of this research was to acquire information about consistency of ZTD (zenith total delay) linear trends and seasonal components between two consecutive GPS reprocessing campaigns. The analysis concerned two sets of the ZTD time series which were estimated during EUREF (Reference Frame Sub-Commission for Europe) EPN (Permanent Network) reprocessing campaigns according to 2008 and 2015 MUT AC (Military University of Technology Analysis Centre) scenarios. Firstly, Lomb–Scargle periodograms were generated for 57 EPN stations to obtain a characterisation of oscillations occurring in the ZTD time series. Then, the values of seasonal components and linear trends were estimated using the LSE (least squares estimation) approach. The Mann–Kendall trend test was also carried out to verify the presence of linear long-term ZTD changes. Finally, differences in seasonal signals and linear trends between these two data sets were investigated. All these analyses were conducted for the ZTD time series of two lengths: a shortened 16-year series and a full 18-year one. In the case of spectral analysis, amplitudes of the annual and semi-annual periods were almost exactly the same for both reprocessing campaigns. Exceptions were found for only a few stations and they did not exceed 1 mm. The estimated trends were also similar. However, for the reprocessing performed in 2008, the trends values were usually higher. In general, shortening of the analysed time period by 2 years resulted in a decrease of the linear trends values of about 0.07 mm yr−1. This was confirmed by analyses based on two data sets.

Rainfall trends in hindsight and in foresight

2020 ◽  
Author(s):  
Theano Iliopoulou ◽  
Demetris Koutsoyiannis
Keyword(s):  
Daily Rainfall ◽  
Predictive Performance ◽  
Predictive Modelling ◽  
Annual Rainfall ◽  
Methodological Framework ◽  
Rainfall Trends ◽  
Out Of Sample ◽  
Linear Trends

<p>Trends are customarily identified in rainfall data in the framework of explanatory modelling. Little insight however has been gained by this type of analysis with respect to their performance in foresight. In this work, we examine the out-of-sample predictive performance of linear trends through extensive investigation of 60 of the longest daily rainfall records available worldwide. We devise a systematic methodological framework in which linear trends are compared to simpler mean models, based on their performance in predicting climatic-scale (30-year) annual rainfall indices, i.e. maxima, totals, wet-day average and probability dry, from long-term daily records. Parallel experiments from synthetic timeseries are performed in order to provide theoretical insights to the results and the role of parsimony in predictive modelling is discussed. In line with the empirical findings, it is shown that, prediction-wise, simple is preferable to trendy.</p>

On records and related processes for sequences with trends

1999 ◽  
Vol 36 (3) ◽  
pp. 668-681 ◽  
Author(s):  
K. Borovkov
Keyword(s):  
Markov Chains ◽  
Limit Theorems ◽  
Convergence Rates ◽  
Rate Function ◽  
Regular Case ◽  
Limiting Distributions ◽  
Record Times ◽  
Ergodicity Theorem ◽  
Linear Trends

We study the records and related variables for sequences with linear trends. We discuss the properties of the asymptotic rate function and relationships between the distribution of the long-term maxima in the sequence and that of a particular observation, including two characterization type results. We also consider certain Markov chains related to the process of records and prove limit theorems for them, including the ergodicity theorem in the regular case (convergence rates are given under additional assumptions), and derive the limiting distributions for the inter-record times and increments of records.

scholarly journals EFFECTS OF POLYNOMIAL TRENDS ON DETRENDING MOVING AVERAGE ANALYSIS

Fractals ◽  
2015 ◽  
Vol 23 (03) ◽  
pp. 1550034 ◽  
Author(s):  
YING-HUI SHAO ◽  
GAO-FENG GU ◽  
ZHI-QIANG JIANG ◽  
WEI-XING ZHOU
Keyword(s):  
Time Series ◽  
Numerical Experiments ◽  
Moving Average ◽  
Nonstationary Time Series ◽  
Hurst Index ◽  
Forward Algorithm ◽  
Average Analysis ◽  
Polynomial Trends ◽  
Linear Trends

The detrending moving average (DMA) algorithm is one of the best performing methods to quantify the long-term correlations in nonstationary time series. As many long-term correlated time series in real systems contain various trends, we investigate the effects of polynomial trends on the scaling behaviors and the performances of three widely used DMA methods including backward algorithm (BDMA), centered algorithm (CDMA) and forward algorithm (FDMA). We derive a general framework for polynomial trends and obtain analytical results for constant shifts and linear trends. We find that the behavior of the CDMA method is not influenced by constant shifts. In contrast, linear trends cause a crossover in the CDMA fluctuation functions. We also find that constant shifts and linear trends cause crossovers in the fluctuation functions obtained from the BDMA and FDMA methods. When a crossover exists, the scaling behavior at small scales comes from the intrinsic time series while that at large scales is dominated by the constant shifts or linear trends. We also derive analytically the expressions of crossover scales and show that the crossover scale depends on the strength of the polynomial trends, the Hurst index, and in some cases (linear trends for BDMA and FDMA) the length of the time series. In all cases, the BDMA and the FDMA behave almost the same under the influence of constant shifts or linear trends. Extensive numerical experiments confirm excellently the analytical derivations. We conclude that the CDMA method outperforms the BDMA and FDMA methods in the presence of polynomial trends.

scholarly journals Analysing Hydrometeorological Time Series for Evidence of Climatic Change

1993 ◽  
Vol 24 (2-3) ◽  
pp. 135-150 ◽  
Author(s):  
Geoff Kite
Keyword(s):  
Time Series ◽  
Climatic Change ◽  
Data Sets ◽  
Large Lakes ◽  
Hydrologic Time Series ◽  
Natural Time ◽  
Streamflow Data ◽  
Scientific Attention ◽  
Linear Trends

Considerable scientific attention has been focused on a measured increase in atmospheric CO2 and a suspected corresponding change in climate. Such a change in climate, if it occurred, might be expected to have a magnified effect on hydrologic time series and, indeed, projections have been made of major changes in water resources. If the climatic changes are indeed magnified in hydrologic time series then, by detecting trends in such series, it should be possible to work backwards and identify the causative climatic change. This paper looks at two data sets: 1) long-term temperature, precipitation and streamflow data from sites across Canada and 2) long-term levels of large lakes in Africa and North America. The study assumes that time series may be modelled by trend, periodic, autoregressive and random residual components. The trend component of a time series is generally associated with changes in the structure of the time series caused by cumulative natural or manmade phenomena. Periodicities in natural time series are usually due to astronomical cycles such as the earth's rotation around the sun. Autoregressive components reflect the tendency for an event to be dependent on the magnitude of the previous event(s), a memory effect. The analyses of temperature, precipitation and streamflow data show some significant linear trends but no pattern is apparent. The analyses of longterm lake levels also identify linear trends but these are all explainable without invoking climate change due to greenhouse gases.

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