scholarly journals Stratigraphic Noise in Time Series Derived from Ice Cores

1985 ◽  
Vol 7 ◽  
pp. 76-83 ◽  
Author(s):  
David A. Fisher ◽  
Niels Reeh ◽  
H.B. Clausen
Keyword(s):  
Time Series ◽  
Spectral Power ◽  
Ice Cores ◽  
Noise Element ◽  
High Frequencies ◽  
Important Input ◽  
Time Averages ◽  
Arctic Ice ◽  
Short Time ◽  
Snow Drifting

Because of snow drifting, two time series of any variable derived from two adjacent ice cores will differ considerably. The size and statistical nature of this noise element is discussed for two kinds of measured substance. A theory is developed and compared to data from Greenland and Canadian Arctic ice cores. In case 1, the measured substance can diffuse and the seasonal cycle degrade with time and depth, e.g. δ(18O). In case 2, the measured substance cannot diffuse, e.g. microparticles. The case 2 time series contain drift noise proportional to that in the accumulation series. For accumulation series, the spectral power is concentrated at the high frequencies, i.e. is “blue”. Such noise can be easily reduced by taking relatively short time averages. The noise in the case 1 time series, however, starts out “blue” but quickly diffuses to have a “red” character with significant power at longer wavelengths, and many decades of such series must be averaged to reduce the noise level. Because the seasonal amplitude of any given variable is an important input to the drift noise and because the seasonal amplitudes of some variable types are latitude-dependent, some sites have inherently less drift noise than others.

scholarly journals Stratigraphic Noise in Time Series Derived from Ice Cores

1985 ◽  
Vol 7 ◽  
pp. 76-83 ◽  
Author(s):  
David A. Fisher ◽  
Niels Reeh ◽  
H.B. Clausen
Keyword(s):  
Time Series ◽  
Spectral Power ◽  
Ice Cores ◽  
Noise Element ◽  
High Frequencies ◽  
Important Input ◽  
Time Averages ◽  
Arctic Ice ◽  
Short Time ◽  
Snow Drifting

Because of snow drifting, two time series of any variable derived from two adjacent ice cores will differ considerably. The size and statistical nature of this noise element is discussed for two kinds of measured substance. A theory is developed and compared to data from Greenland and Canadian Arctic ice cores. In case 1, the measured substance can diffuse and the seasonal cycle degrade with time and depth, e.g. δ(18O). In case 2, the measured substance cannot diffuse, e.g. microparticles. The case 2 time series contain drift noise proportional to that in the accumulation series. For accumulation series, the spectral power is concentrated at the high frequencies, i.e. is “blue”. Such noise can be easily reduced by taking relatively short time averages. The noise in the case 1 time series, however, starts out “blue” but quickly diffuses to have a “red” character with significant power at longer wavelengths, and many decades of such series must be averaged to reduce the noise level. Because the seasonal amplitude of any given variable is an important input to the drift noise and because the seasonal amplitudes of some variable types are latitude-dependent, some sites have inherently less drift noise than others.

Reconstructed Summer Degree Days in Central Alaska and Northwestern Canada since 1524

1985 ◽  
Vol 23 (1) ◽  
pp. 18-26 ◽  
Author(s):  
Gordon C. Jacoby ◽  
Edward R. Cook ◽  
Linda D. Ulan
Keyword(s):  
Time Series ◽  
Energy Budget ◽  
General Circulation ◽  
Meteorological Data ◽  
Ice Cores ◽  
Warming Trend ◽  
Quantitative Information ◽  
Degree Days ◽  
Time Periods ◽  
Arctic Ice

Three time series based on precisely dated annual tree-ring widths have been used to reconstruct June plus July degree days for the central Alaska and northwestern Canada region. The time series are the longest recently developed chronologies for the area and represent 57 core samples from 27 trees. The degree-year-to-year variation and day reconstruction, extending back to A.D. 1524, exhibits much extended warming and cooling trends including a general warming trend from about 1840 to 1960. The reconstruction is in agreement with some subaretic glacial information and with data of percentage melting from arctic ice cores. This and similar reconstructions can provide quantitative information to compare with general circulation and energy budget models for longer time periods than are available in recorded meteorological data.

scholarly journals Identifying bidirectional total and non-linear information flow in functional corticomuscular coupling during a dorsiflexion task: a pilot study

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Tie Liang ◽  
Qingyu Zhang ◽  
Xiaoguang Liu ◽  
Bin Dong ◽  
Xiuling Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Information Flow ◽  
Gamma Band ◽  
Motor Dysfunction ◽  
Beta Band ◽  
Short Time Series ◽  
Information Interaction ◽  
Stroke Group ◽  
Short Time ◽  
Maximal Information Coefficient

Abstract Background The key challenge to constructing functional corticomuscular coupling (FCMC) is to accurately identify the direction and strength of the information flow between scalp electroencephalography (EEG) and surface electromyography (SEMG). Traditional TE and TDMI methods have difficulty in identifying the information interaction for short time series as they tend to rely on long and stable data, so we propose a time-delayed maximal information coefficient (TDMIC) method. With this method, we aim to investigate the directional specificity of bidirectional total and nonlinear information flow on FCMC, and to explore the neural mechanisms underlying motor dysfunction in stroke patients. Methods We introduced a time-delayed parameter in the maximal information coefficient to capture the direction of information interaction between two time series. We employed the linear and non-linear system model based on short data to verify the validity of our algorithm. We then used the TDMIC method to study the characteristics of total and nonlinear information flow in FCMC during a dorsiflexion task for healthy controls and stroke patients. Results The simulation results showed that the TDMIC method can better detect the direction of information interaction compared with TE and TDMI methods. For healthy controls, the beta band (14–30 Hz) had higher information flow in FCMC than the gamma band (31–45 Hz). Furthermore, the beta-band total and nonlinear information flow in the descending direction (EEG to EMG) was significantly higher than that in the ascending direction (EMG to EEG), whereas in the gamma band the ascending direction had significantly higher information flow than the descending direction. Additionally, we found that the strong bidirectional information flow mainly acted on Cz, C3, CP3, P3 and CPz. Compared to controls, both the beta-and gamma-band bidirectional total and nonlinear information flows of the stroke group were significantly weaker. There is no significant difference in the direction of beta- and gamma-band information flow in stroke group. Conclusions The proposed method could effectively identify the information interaction between short time series. According to our experiment, the beta band mainly passes downward motor control information while the gamma band features upward sensory feedback information delivery. Our observation demonstrate that the center and contralateral sensorimotor cortex play a major role in lower limb motor control. The study further demonstrates that brain damage caused by stroke disrupts the bidirectional information interaction between cortex and effector muscles in the sensorimotor system, leading to motor dysfunction.

scholarly journals The influence of the lunar nodal cycle on Arctic climate

2006 ◽  
Vol 63 (3) ◽  
pp. 401-420 ◽  
Author(s):  
Harald Yndestad
Keyword(s):  
Time Series ◽  
Signal To Noise Ratio ◽  
Phase Relationship ◽  
Harmonic Spectrum ◽  
Arctic Climate ◽  
The Arctic ◽  
Major Influence ◽  
Sea Temperature ◽  
Arctic Ice

Abstract The Arctic Ocean is a substantial energy sink for the northern hemisphere. Fluctuations in its energy budget will have a major influence on the Arctic climate. The paper presents an analysis of the time-series for the polar position, the extent of Arctic ice, sea level at Hammerfest, Kola section sea temperature, Røst winter air temperature, and the NAO winter index as a way to identify a source of dominant cycles. The investigation uses wavelet transformation to identify the period and the phase in these Arctic time-series. System dynamics are identified by studying the phase relationship between the dominant cycles in all time-series. A harmonic spectrum from the 18.6-year lunar nodal cycle in the Arctic time-series has been identified. The cycles in this harmonic spectrum have a stationary period, but not stationary amplitude and phase. A sub-harmonic cycle of about 74 years may introduce a phase reversal of the 18.6-year cycle. The signal-to-noise ratio between the lunar nodal spectrum and other sources changes from 1.6 to 3.2. A lunar nodal cycle in all time-series indicates that there is a forced Arctic oscillating system controlled by the pull of gravity from the moon, a system that influences long-term fluctuations in the extent of Arctic ice. The phase relation between the identified cycles indicates a possible chain of events from lunar nodal gravity cycles, to long-term tides, polar motions, Arctic ice extent, the NAO winter index, weather, and climate.

Ultradian rhythms of autonomic, cardiovascular, and neuroendocrine systems are related in humans

1996 ◽  
Vol 270 (4) ◽  
pp. R873-R887 ◽  
Author(s):  
D. S. Shannahoff-Khalsa ◽  
B. Kennedy ◽  
F. E. Yates ◽  
M. G. Ziegler
Keyword(s):  
Spectral Power ◽  
Correlation Coefficients ◽  
Autonomic Tone ◽  
Ejection Time ◽  
Index Stroke Volume ◽  
Nasal Cycle ◽  
Index Stroke ◽  
Fast Orthogonal Search ◽  
Time Averages ◽  
Ventricular Ejection Time

Autonomic, cardiovascular, and neuroendocrine activities were monitored for 5-6 h in 10 normal adult resting humans (8 males, 2 females). The nasal cycle, a measure of lateralized autonomic tone, was measured at 4 Hz. Impedance cardiography (BoMed NCCOM3) was used to measure cardiac output, thoracic fluid index, heart rate, ejection velocity index, stroke volume, and ventricular ejection time (averages of 12 heart beats). Systolic, diastolic, and mean arterial pressures were measured with an automated cuff at 7.5-min intervals. Separate blood samples were taken every 7.5 min simultaneously from both arms with the use of indwelling venous catheters. Assays for adrenocorticotropic hormone, luteinizing hormone, norepinephrine, epinephrine, and dopamine were performed on samples from each arm. Time-series analysis, using the fast orthogonal search method of Korenberg, was used to detect variance structure. Significant spectral periods were observed in five windows at 220-340, 170-215, 115-145, 70-100, and 40-65 min. The greatest spectral power was observed in the lower frequencies, but periods at 115-145, 70-100, and 40-65 min were common across variables. Significant correlation coefficients for linear regressions of all paired variables in each subject were observed in 38.87% of the comparisons (subject range, 18.05-48-9.70%) with r > 0.30. These results suggest that either a common oscillator (the hypothalamus) or mutually entrained oscillators regulate these systems.

scholarly journals Extracting Features from Time Series

2018 ◽  
pp. 85-100 ◽  
Author(s):  
Christian Herff ◽  
Dean J. Krusienski
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Clinical Data ◽  
Noise Filtering ◽  
Time Intervals ◽  
Time Points ◽  
Biomedical Systems ◽  
Patient Weight ◽  
Short Time

AbstractClinical data is often collected and processed as time series: a sequence of data indexed by successive time points. Such time series can be from sources that are sampled over short time intervals to represent continuous biophysical wave-(one word waveforms) forms such as the voltage measurements representing the electrocardiogram, to measurements that are sampled daily, weekly, yearly, etc. such as patient weight, blood triglyceride levels, etc. When analyzing clinical data or designing biomedical systems for measurements, interventions, or diagnostic aids, it is important to represent the information contained within such time series in a more compact or meaningful form (e.g., noise filtering), amenable to interpretation by a human or computer. This process is known as feature extraction. This chapter will discuss some fundamental techniques for extracting features from time series representing general forms of clinical data.

scholarly journals A reliable measure of similarity based on dependency for short time series: an application to gene expression networks

2009 ◽  
Vol 10 (1) ◽  
pp. 270 ◽  
Author(s):  
Mônica G Campiteli ◽  
Frederico M Soriani ◽  
Iran Malavazi ◽  
Osame Kinouchi ◽  
Carlos AB Pereira ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Reliable Measure ◽  
Short Time Series ◽  
Measure Of Similarity ◽  
Short Time

scholarly journals Detecting dynamical anomalies in time series from different palaeoclimate proxy archives using windowed recurrence network analysis

2019 ◽  
Author(s):  
Jaqueline Lekscha ◽  
Reik V. Donner
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Nonlinear Filtering ◽  
Ice Cores ◽  
Second Step ◽  
Different Types ◽  
Output Time ◽  
Input Time ◽  
Past Climate Variability ◽  
Input Time Series

Abstract. Analysing palaeoclimate proxy time series using windowed recurrence network analysis (wRNA) has been shown to provide valuable information on past climate variability. In turn, it has also been found that the robustness of the obtained results differs among proxies from different palaeoclimate archives. To systematically test the suitability of wRNA for studying different types of palaeoclimate proxy time series, we use the framework of forward proxy modelling. For this, we create artificial input time series with different properties and, in a first step, compare the time series properties of the input and the model output time series. In a second step, we compare the areawise significant anomalies detected using wRNA. For proxies from tree and lake archives, we find that significant anomalies present in the input time series are sometimes missed in the input time series after the nonlinear filtering by the corresponding models. For proxies from speleothems, we observe falsely identified significant anomalies that are not present in the input time series. Finally, for proxies from ice cores, the wRNA results show the best correspondence with those for the input data. Our results contribute to improve the interpretation of windowed recurrence network analysis results obtained from real-world palaeoclimate time series.

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