scholarly journals The CH-IRP data set: a decade of fortnightly data on <i>δ</i><sup>2</sup>H and <i>δ</i><sup>18</sup>O in streamflow and precipitation in Switzerland

2020 ◽  
Vol 12 (4) ◽  
pp. 3057-3066
Author(s):  
Maria Staudinger ◽  
Stefan Seeger ◽  
Barbara Herbstritt ◽  
Michael Stoelzle ◽  
Jan Seibert ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Isotope Composition ◽  
Series Data ◽  
Data Repository ◽  
Data Sets ◽  
Individual Study ◽  
Data Set ◽  
Flow Pathways

Abstract. The stable isotopes of oxygen and hydrogen, 18O and 2H, provide information on water flow pathways and hydrologic catchment functioning. Here a data set of time series data on precipitation and streamflow isotope composition in medium-sized Swiss catchments, CH-IRP, is presented that is unique in terms of its long-term multi-catchment coverage along an alpine to pre-alpine gradient. The data set comprises fortnightly time series of both δ2H and δ18O as well as deuterium excess from streamflow for 23 sites in Switzerland, together with summary statistics of the sampling at each station. Furthermore, time series of δ18O and δ2H in precipitation are provided for each catchment derived from interpolated data sets from the ISOT, GNIP and ANIP networks. For each station we compiled relevant metadata describing both the sampling conditions and catchment characteristics and climate information. Lab standards and errors are provided, and potentially problematic measurements are indicated to help the user decide on the applicability for individual study purposes. For the future, the measurements are planned to be continued at 14 stations as a long-term isotopic measurement network, and the CH-IRP data set will, thus, continuously be extended. The data set can be downloaded from data repository Zenodo at https://doi.org/10.5281/zenodo.4057967 (Staudinger et al., 2020).

scholarly journals The CH-IRP data set: a decade of fortnightly data of δ<sup>2</sup>H and δ<sup>18</sup>O in streamflow and precipitation in Switzerland

2020 ◽  
Author(s):  
Maria Staudinger ◽  
Stefan Seeger ◽  
Barbara Herbstritt ◽  
Michael Stoelzle ◽  
Jan Seibert ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Isotope Composition ◽  
Series Data ◽  
Data Repository ◽  
Summary Statistics ◽  
Individual Study ◽  
Data Set ◽  
Flow Pathways

Abstract. The stable isotopes of oxygen and hydrogen, 2H and 18O, provide information on water flow pathways and hydrologic catchment functioning. Here a data set of time series data on precipitation and streamflow isotope composition in Swiss medium-sized catchments, CH-IRP, is presented that is unique in terms of its long-term multi-catchment coverage along an alpine to pre-alpine gradient. The data set comprises fortnightly time series of both δ2H and δ18O as well as Deuterium excess from streamflow for 23 sites in Switzerland, together with summary statistics of the sampling at each station. Furthermore, time series of δ18O and δ2H in precipitation are provided for each catchment derived from interpolated datasets from the NISOT, GNIP and ANIP networks. For each station we compiled relevant metadata describing both the sampling conditions as well as catchment characteristics and climate infomation. Lab standards and errors are provided, and potentially problematic measurements are indicated to help the user decide on the applicability for individual study purposes. For the future, it is planned that the measurements will be continued at 14 stations as a long-term isotopic measurement network and the CH-IRP data set will, thus, be continuously be extended. The data set can be downloaded from data repository zenodo https://doi.org/10.5281/zenodo.3659679 (Staudinger et al., 2020).

scholarly journals Time series event correlation with DTW and Hierarchical Clustering methods

2019 ◽  
Author(s):  
Srishti Mishra ◽  
Zohair Shafi ◽  
Santanu Pathak
Keyword(s):  
Time Series ◽  
Hierarchical Clustering ◽  
Time Series Data ◽  
Series Data ◽  
Data Sets ◽  
Multiple Time ◽  
Clustering Methods ◽  
Event Correlation ◽  
Data Set ◽  
Causation Analysis

Data driven decision making is becoming increasingly an important aspect for successful business execution. More and more organizations are moving towards taking informed decisions based on the data that they are generating. Most of this data are in temporal format - time series data. Effective analysis across time series data sets, in an efficient and quick manner is a challenge. The most interesting and valuable part of such analysis is to generate insights on correlation and causation across multiple time series data sets. This paper looks at methods that can be used to analyze such data sets and gain useful insights from it, primarily in the form of correlation and causation analysis. This paper focuses on two methods for doing so, Two Sample Test with Dynamic Time Warping and Hierarchical Clustering and looks at how the results returned from both can be used to gain a better understanding of the data. Moreover, the methods used are meant to work with any data set, regardless of the subject domain and idiosyncrasies of the data set, primarily, a data agnostic approach.

scholarly journals Subsequence Time Series Clustering

2011 ◽  
pp. 1871-1876
Author(s):  
Jason Chen
Keyword(s):  
Time Series ◽  
Traditional Method ◽  
Time Series Data ◽  
Large Data ◽  
Series Data ◽  
Data Sets ◽  
Data Set ◽  
Time Series Clustering ◽  
Mining Community ◽  
Representative Points

Clustering analysis is a tool used widely in the Data Mining community and beyond (Everitt et al. 2001). In essence, the method allows us to “summarise” the information in a large data set X by creating a very much smaller set C of representative points (called centroids) and a membership map relating each point in X to its representative in C. An obvious but special type of data set that one might want to cluster is a time series data set. Such data has a temporal ordering on its elements, in contrast to non-time series data sets. In this article we explore the area of time series clustering, focusing mainly on a surprising recent result showing that the traditional method for time series clustering is meaningless. We then survey the literature of recent papers and go on to argue how time series clustering can be made meaningful.

scholarly journals A Review on Anomaly Detection in Time Series

2021 ◽  
Vol 10 (3) ◽  
pp. 1895-1900
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Monitoring Network ◽  
Series Data ◽  
Data Sets ◽  
Data Set ◽  
The Past ◽  
Machine Health Monitoring ◽  
Machine Health

Time series is a very common class of data sets. Among others, it is very simple to obtain time series data from a variety of various science and finance applications and an anomaly detection technique for time series is becoming a very prominent research topic nowadays. Anomaly identification covers intrusion detection, detection of theft, mistake detection, machine health monitoring, network sensor event detection or habitat disturbance. It is also used for removing suspicious data from the data set before production. This review aims to provide a detailed and organized overview of the Anomaly detection investigation. In this article we will first define what an anomaly in time series is, and then describe quickly some of the methods suggested in the past two or three years for detection of anomaly in time series

scholarly journals Case Studies in Multi-unit LongitudinalModels with Random Coefficients and Patterned Correlation Structure

2016 ◽  
Vol 29 (2) ◽  
pp. 93-110
Author(s):  
Johannes Ledolter
Keyword(s):  
Time Series ◽  
Model Building ◽  
Time Series Data ◽  
Random Coefficients ◽  
Correlation Structure ◽  
Model Specification ◽  
Series Data ◽  
Data Sets ◽  
Economic Time Series ◽  
Data Set

Modelling issues in multi-unit longitudinal models with random coefficients and patterned correlation structure are illustrated in the context of three data sets. The first data set deals with short time series data on annual death rates and alcohol consumption of twenty-five European countries. The second data set deals with glaceologic time series data on snow temperature at 14 different locations within a small glacier in the Austrian Alps. The third data set consists of annual economic time series on factor productivity, anddomestic and foreign research/development (R&D) capital stocks. A practical model building approach–consisting of model specification, estimation, and diagnostic checking–is outlined in the context of these three data sets.

scholarly journals Time series event correlation with DTW and Hierarchical Clustering methods

2019 ◽  
Author(s):  
Srishti Mishra ◽  
Zohair Shafi ◽  
Santanu Pathak
Keyword(s):  
Time Series ◽  
Hierarchical Clustering ◽  
Time Series Data ◽  
Series Data ◽  
Data Sets ◽  
Multiple Time ◽  
Clustering Methods ◽  
Event Correlation ◽  
Data Set ◽  
Causation Analysis

Data driven decision making is becoming increasingly an important aspect for successful business execution. More and more organizations are moving towards taking informed decisions based on the data that they are generating. Most of this data are in temporal format - time series data. Effective analysis across time series data sets, in an efficient and quick manner is a challenge. The most interesting and valuable part of such analysis is to generate insights on correlation and causation across multiple time series data sets. This paper looks at methods that can be used to analyze such data sets and gain useful insights from it, primarily in the form of correlation and causation analysis. This paper focuses on two methods for doing so, Two Sample Test with Dynamic Time Warping and Hierarchical Clustering and looks at how the results returned from both can be used to gain a better understanding of the data. Moreover, the methods used are meant to work with any data set, regardless of the subject domain and idiosyncrasies of the data set, primarily, a data agnostic approach.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

Welfare Use in Japan: Trends and Determinants

2007 ◽  
pp. 88
Author(s):  
Wataru Suzuki ◽  
Yanfei Zhou
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Negative Effects ◽  
Long Time ◽  
Welfare Use ◽  
The Impact

This article represents the first step in filling a large gap in knowledge concerning why Public Assistance (PA) use recently rose so fast in Japan. Specifically, we try to address this problem not only by performing a Blanchard and Quah decomposition on long-term monthly time series data (1960:04-2006:10), but also by estimating prefecturelevel longitudinal data. Two interesting findings emerge from the time series analysis. The first is that permanent shock imposes a continuously positive impact on the PA rate and is the main driving factor behind the recent increase in welfare use. The second finding is that the impact of temporary shock will last for a long time. The rate of the use of welfare is quite rigid because even if the PA rate rises due to temporary shocks, it takes about 8 or 9 years for it to regain its normal level. On the other hand, estimations of prefecture-level longitudinal data indicate that the Financial Capability Index (FCI) of the local government2 and minimum wage both impose negative effects on the PA rate. We also find that the rapid aging of Japan's population presents a permanent shock in practice, which makes it the most prominent contribution to surging welfare use.

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