scholarly journals Interpretation of the Chemical and Physical Time-Series Retrieved from Sentik Glacier, Ladakh Himalaya, India

1984 ◽  
Vol 30 (104) ◽  
pp. 66-76 ◽  
Author(s):  
Paul A. Mayewski ◽  
W. Berry Lyons ◽  
N. Ahmad ◽  
Gordon Smith ◽  
M. Pourchet
Keyword(s):  
Time Series ◽  
Chemical Species ◽  
Data Sets ◽  
Reactive Iron ◽  
Physical Time ◽  
Ladakh Himalaya ◽  
Data Density ◽  
Mass Circulation ◽  
The Himalaya ◽  
Analysis Of Time Series

AbstractSpectral analysis of time series of a c. 17 ± 0.3 year core, calibrated for total ß activity recovered from Sentik Glacier (4908m) Ladakh, Himalaya, yields several recognizable periodicities including subannual, annual, and multi-annual. The time-series, include both chemical data (chloride, sodium, reactive iron, reactive silicate, reactive phosphate, ammonium, δD, δ(18O) and pH) and physical data (density, debris and ice-band locations, and microparticles in size grades 0.50 to 12.70 μm). Source areas for chemical species investigated and general air-mass circulation defined from chemical and physical time-series are discussed to demonstrate the potential of such studies in the development of paleometeorological data sets from remote high-alpine glacierized sites such as the Himalaya.

scholarly journals Interpretation of the Chemical and Physical Time-Series Retrieved from Sentik Glacier, Ladakh Himalaya, India

1984 ◽  
Vol 30 (104) ◽  
pp. 66-76 ◽  
Author(s):  
Paul A. Mayewski ◽  
W. Berry Lyons ◽  
N. Ahmad ◽  
Gordon Smith ◽  
M. Pourchet
Keyword(s):  
Time Series ◽  
Chemical Species ◽  
Data Sets ◽  
Reactive Iron ◽  
Physical Time ◽  
Ladakh Himalaya ◽  
Data Density ◽  
Mass Circulation ◽  
The Himalaya ◽  
Analysis Of Time Series

AbstractSpectral analysis of time series of ac. 17 ± 0.3 year core, calibrated for totalßactivity recovered from Sentik Glacier (4908m) Ladakh, Himalaya, yields several recognizable periodicities including subannual, annual, and multi-annual. The time-series, include both chemical data (chloride, sodium, reactive iron, reactive silicate, reactive phosphate, ammonium,δD,δ(18O) and pH) and physical data (density, debris and ice-band locations, and microparticles in size grades 0.50 to 12.70μm). Source areas for chemical species investigated and general air-mass circulation defined from chemical and physical time-series are discussed to demonstrate the potential of such studies in the development of paleometeorological data sets from remote high-alpine glacierized sites such as the Himalaya.

scholarly journals Can High-Altitude Ice Masses in Temperate Areas Provide Useful Climatic Records?

1985 ◽  
Vol 7 ◽  
pp. 89-89
Author(s):  
Paul A. Mayewski ◽  
W. Berry Lyons
Keyword(s):  
High Altitude ◽  
Ice Core ◽  
Chemical Species ◽  
Data Sets ◽  
Air Masses ◽  
Reactive Iron ◽  
Core Recovery ◽  
Relative Dating ◽  
Himalayan Glaciers ◽  
Activity Density

Since 1979 we have been conducting a program of glaciochemical sampling and analysis in selected portions of the Indian Himalayas. The primary purpose of this work has been the retrieval of data that are of specific use in assessing the signal expressed by the chemistry of air masses entering the Himalayas. The techniques used for this purpose provide data sets for the following: chloride, sodium, reactive iron, reactive silicate, reactive phosphate, nitrite-plus-nitrate, ammonium, pH, oxygen isotopes, deuterium, microparticles, total β-activity, density and scanning electron microscopy. The results of this work appear in a series of papers (Lyons and others 1981, Lyons and Mayewski 1983, Mayewski and others 1981, 1983, 1984 and Goss and others 1985). In summary this work demonstrates: (1) problems encountered in high-altitude ice-core recovery, (2) effects of percolation on chemical records, (3) specific requirements necessary for the retrieval of unaltered glaciochemical records from Himalayan glaciers, (4) potential spatial variability of chemical species concentrations and interpretation of this with respect to time series, (5) usefulness of various glaciochemical indicators as applied to relative dating (seasonality) and air mass tracking, (6) specific details of the chemical and physical properties in Himalayan ice, and (7) recommendations for future Himalayan ice-core studies.

scholarly journals Can High-Altitude Ice Masses in Temperate Areas Provide Useful Climatic Records?

1985 ◽  
Vol 7 ◽  
pp. 89
Author(s):  
Paul A. Mayewski ◽  
W. Berry Lyons
Keyword(s):  
High Altitude ◽  
Ice Core ◽  
Chemical Species ◽  
Data Sets ◽  
Air Masses ◽  
Reactive Iron ◽  
Core Recovery ◽  
Relative Dating ◽  
Himalayan Glaciers ◽  
Activity Density

Since 1979 we have been conducting a program of glaciochemical sampling and analysis in selected portions of the Indian Himalayas. The primary purpose of this work has been the retrieval of data that are of specific use in assessing the signal expressed by the chemistry of air masses entering the Himalayas. The techniques used for this purpose provide data sets for the following: chloride, sodium, reactive iron, reactive silicate, reactive phosphate, nitrite-plus-nitrate, ammonium, pH, oxygen isotopes, deuterium, microparticles, total β-activity, density and scanning electron microscopy. The results of this work appear in a series of papers (Lyons and others 1981, Lyons and Mayewski 1983, Mayewski and others 1981, 1983, 1984 and Goss and others 1985). In summary this work demonstrates: (1) problems encountered in high-altitude ice-core recovery, (2) effects of percolation on chemical records, (3) specific requirements necessary for the retrieval of unaltered glaciochemical records from Himalayan glaciers, (4) potential spatial variability of chemical species concentrations and interpretation of this with respect to time series, (5) usefulness of various glaciochemical indicators as applied to relative dating (seasonality) and air mass tracking, (6) specific details of the chemical and physical properties in Himalayan ice, and (7) recommendations for future Himalayan ice-core studies.

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

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