scholarly journals Multi-year high-frequency physical and environmental observations at the Guadiana Estuary

2015 ◽  
Vol 7 (2) ◽  
pp. 299-309 ◽  
Author(s):  
E. Garel ◽  
Ó. Ferreira
Keyword(s):  
High Frequency ◽  
Time Series Data ◽  
Acoustic Doppler Current Profiler ◽  
Time Frame ◽  
Series Data ◽  
Data Set ◽  
Discharge Data ◽  
Current Profiler ◽  
Machine Readable ◽  
Guadiana Estuary

Abstract. High-frequency data collected continuously over a multi-year time frame are required for investigating the various agents that drive ecological and hydrodynamic processes in estuaries. Here, we present water quality and current in situ observations from a fixed monitoring station operating from 2008 to 2014 in the lower Guadiana Estuary, southern Portugal (37°11.30' N, 7°24.67' W). The data were recorded by a multi-parametric probe providing hourly records (temperature, salinity, chlorophyll, dissolved oxygen, turbidity, and pH) at a water depth of ~ 1 m, and by a bottom-mounted acoustic Doppler current profiler measuring the pressure, near-bottom temperature, and flow velocity through the water column every 15 min. The time series data, in particular the probe ones, present substantial gaps arising from equipment failure and maintenance, which are ineluctable with this type of observation in harsh environments. However, prolonged (months-long) periods of multi-parametric observations during contrasted external forcing conditions are available. The raw data are reported together with flags indicating the quality status of each record. River discharge data from two hydrographic stations located near the estuary head are also provided to support data analysis and interpretation. The data set is publicly available in machine-readable format at PANGAEA (doi:10.1594/PANGAEA.845750).

scholarly journals Multiyear high-frequency physical and environmental observations at the Guadiana Estuary

2015 ◽  
Vol 8 (2) ◽  
pp. 567-586
Author(s):  
E. Garel ◽  
Ó. Ferreira
Keyword(s):  
High Frequency ◽  
Acoustic Doppler Current Profiler ◽  
Time Frame ◽  
Discharge Data ◽  
The Status ◽  
Current Profiler ◽  
Hydrodynamic Processes ◽  
Southern Portugal ◽  
Machine Readable ◽  
Guadiana Estuary

Abstract. High-frequency data collected continuously over a multiyear time frame are required for investigating the various agents that drive ecological and hydrodynamic processes in estuaries. Here, we present water quality and current in-situ observations from a fixed monitoring station operating from 2008 to 2014 in the lower Guadiana Estuary, southern Portugal (37°11.30' N, 7°24.67' W). The data were recorded by: a multi-parametric probe providing hourly records of temperature, chlorophyll, dissolved oxygen, turbidity, and pH at a water depth of ~ 1 m; and, a bottom-mounted acoustic Doppler current profiler measuring the pressure, near-bottom temperature, and flow velocity through the water column every 15 min. The time-series, in particular the probe one, present substantial data gaps arising from equipment failure and maintenance, which are ineluctable with this type of observations in harsh environments. However, prolonged (months-long) periods of observations during contrasted external forcing conditions are available. The raw data are reported together with quality flags indicating the status (valid/non-valid) of each record. Hourly river discharge data from two hydrographic stations located near the estuary head are also provided to support data analysis and interpretation. The dataset is publicly available at PANGAEA (doi:10.1594/PANGAEA.845750) in machine-readable format.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Does Tax Increment Financing Pass the “But-for” Test in Missouri?

2019 ◽  
Vol 33 (3) ◽  
pp. 187-202
Author(s):  
Ahmed Rachid El-Khattabi ◽  
T. William Lester
Keyword(s):  
Economic Development ◽  
Kansas City ◽  
Time Series Data ◽  
Local Level ◽  
Conclusive Evidence ◽  
Series Data ◽  
Tax Increment Financing ◽  
Data Set ◽  
Development Indicators ◽  
The Impact

The use of tax increment financing (TIF) remains a popular, yet highly controversial, tool among policy makers in their efforts to promote economic development. This study conducts a comprehensive assessment of the effectiveness of Missouri’s TIF program, specifically in Kansas City and St. Louis, in creating economic opportunities. We build a time-series data set starting 1990 through 2012 of detailed employment levels, establishment counts, and sales at the census block-group level to run a set of difference-in-differences with matching estimates for the impact of TIF at the local level. Although we analyze the impact of TIF on a wide set of indicators and across various industry sectors, we find no conclusive evidence that the TIF program in either city has a causal impact on key economic development indicators.

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.

LamaH: Large-sample Data for Hydrology in Central Europe

2021 ◽  
Author(s):  
Christoph Klingler ◽  
Mathew Herrnegger ◽  
Frederik Kratzert ◽  
Karsten Schulz
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Vegetation Indices ◽  
River Network ◽  
Series Data ◽  
High Temporal Resolution ◽  
Stream Length ◽  
Discharge Data ◽  
Large Sample ◽  
Access To Data

<p>Open large-sample datasets are important for various reasons: i) they enable large-sample analyses, ii) they democratize access to data, iii) they enable large-sample comparative studies and foster reproducibility, and iv) they are a key driver for recent developments of machine-learning based modelling approaches.</p><p>Recently, various large-sample datasets have been released (e.g. different country-specific CAMELS datasets), however, all of them contain only data of individual catchments distributed across entire countries and not connected river networks.</p><p>Here, we present LamaH, a new dataset covering all of Austria and the foreign upstream areas of the Danube, spanning a total of 170.000 km² in 9 different countries with discharge observations for 882 gauges. The dataset also includes 15 different meteorological time series, derived from ERA5-Land, for two different basin delineations: First, corresponding to the entire upstream area of a particular gauge, and second, corresponding only to the area between a particular gauge and its upstream gauges. The time series data for both, meteorological and discharge data, is included in hourly and daily resolution and covers a period of over 35 years (with some exceptions in discharge data for a couple of gauges).</p><p>Sticking closely to the CAMELS datasets, LamaH also contains more than 60 catchment attributes, derived for both types of basin delineations. The attributes include climatic, hydrological and vegetation indices, land cover information, as well as soil, geological and topographical properties. Additionally, the runoff gauges are classified by over 20 different attributes, including information about human impact and indicators for data quality and completeness. Lastly, LamaH also contains attributes for the river network itself, like gauge topology, stream length and the slope between two sequential gauges.</p><p>Given the scope of LamaH, we hope that this dataset will serve as a solid database for further investigations in various tasks of hydrology. The extent of data combined with the interconnected river network and the high temporal resolution of the time series might reveal deeper insights into water transfer and storage with appropriate methods of modelling.</p>

scholarly journals Acoustic and optical methods to infer water transparency at Time Series Station Spiekeroog, Wadden Sea

Ocean Science ◽  
2016 ◽  
Vol 12 (6) ◽  
pp. 1155-1163 ◽  
Author(s):  
Anne-Christin Schulz ◽  
Thomas H. Badewien ◽  
Shungudzemwoyo P. Garaba ◽  
Oliver Zielinski
Keyword(s):  
Time Series ◽  
Wadden Sea ◽  
Acoustic Doppler Current Profiler ◽  
Water Transparency ◽  
Optical Observations ◽  
Optical Methods ◽  
Acoustic Measurements ◽  
Data Set ◽  
Backscatter Signal ◽  
Current Profiler

Abstract. Water transparency is a primary indicator of optical water quality that is driven by suspended particulate and dissolved material. A data set from the operational Time Series Station Spiekeroog located at a tidal inlet of the Wadden Sea was used to perform (i) an inter-comparison of observations related to water transparency, (ii) correlation tests among these measured parameters, and (iii) to explore the utility of both acoustic and optical tools in monitoring water transparency. An Acoustic Doppler Current Profiler was used to derive the backscatter signal in the water column. Optical observations were collected using above-water hyperspectral radiometers and a submerged turbidity metre. Bio-fouling on the turbidity sensors optical windows resulted in measurement drift and abnormal values during quality control steps. We observed significant correlations between turbidity collected by the submerged metre and that derived from above-water radiometer observations. Turbidity from these sensors was also associated with the backscatter signal derived from the acoustic measurements. These findings suggest that both optical and acoustic measurements can be reasonable proxies of water transparency with the potential to mitigate gaps and increase data quality in long-time observation of marine environments.

scholarly journals Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 349-356
Author(s):  
J. HAZARIKA ◽  
B. PATHAK ◽  
A. N. PATOWARY
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Demand Management ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Data Set ◽  
Modeling And Forecasting ◽  
Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points.  We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.  

scholarly journals A New Method to Detect Outliers in High-frequency Time Series

2018 ◽  
Vol 8 (1) ◽  
pp. 16
Author(s):  
Ilaria Lucrezia Amerise ◽  
Agostino Tarsitano
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Time Series Data ◽  
New Method ◽  
Series Data ◽  
Absolute Difference ◽  
Data Preparation ◽  
Simple Method ◽  
Pass Through ◽  
Nonparametric Procedure

The objective of this research is to develop a fast, simple method for detecting and replacing extreme spikes in high-frequency time series data. The method primarily consists  of a nonparametric procedure that pursues a balance between fidelity to observed data and smoothness. Furthermore, through examination of the absolute difference between original and smoothed values, the technique is also able to detect and, where necessary, replace outliers with less extreme data. Unlike other filtering procedures found in the literature, our method does not require a model to be specified for the data. Additionally, the filter makes only a single pass through the time series. Experiments  show that the new method can be validly used as a data preparation tool to ensure that time series modeling is supported by clean data, particularly in a complex context such as one with high-frequency data.

scholarly journals A Method for Processing Acoustic Doppler Current Profiler Velocity Data from Towed, Undulating Vehicles

2008 ◽  
Vol 25 (9) ◽  
pp. 1710-1716 ◽  
Author(s):  
Jiayi Pan ◽  
David A. Jay
Keyword(s):  
Surface Waters ◽  
High Frequency ◽  
Acoustic Doppler Current Profiler ◽  
Small Time ◽  
Ocean Current ◽  
Near Surface ◽  
Vertical Range ◽  
External Reference ◽  
Current Profiler ◽  
Adcp Measurements

Abstract The utility of the acoustic Doppler current profiler (ADCP) for sampling small time and space scales of coastal environments can be enhanced by mounting a high-frequency (1200 kHz) ADCP on an oscillating towed body. This approach requires both an external reference to convert the measured shears to velocities in the earth coordinates and a method to determine the towed body velocities. During the River Influence on the Shelf Ecosystems (RISE) project cruise, a high-frequency (1200 kHz) and narrowbeam ADCP with mode 12 sampling was mounted on a TRIAXUS oscillating towfish, which steers a 3D path behind the ship. This deployment approach extended the vertical range of the ADCP and allowed it to sample near-surface waters outside the ship’s wake. The measurements from a ship-mounted 1200-kHz narrowbeam ADCP are used as references for TRIAXUS ADCP data, and a method of overlapping bins is employed to recover the entire vertical range of the TRIAXUS ADCP. The TRIAXUS vehicle horizontal velocities are obtained by removing the derived ocean current velocity from the TRIAXUS ADCP measurements. The results show that the method is practical.

Do Americans View Financial Threats as Important Political Issues?

2015 ◽  
Author(s):  
Adam Seth Levine
Keyword(s):  
Time Series Data ◽  
Job Insecurity ◽  
Time Frame ◽  
Study Data ◽  
Series Data ◽  
Daily Lives ◽  
Political Issues ◽  
The People ◽  
National Election Study ◽  
The Cost

This chapter describes in greater detail the objective situation facing Americans in four major areas of financial threats: job insecurity, healthcare costs, retirement, and the cost of college. It analyzes the politics of such threats among the mass public. It examines the extent to which the people who consider such issues important are facing them in their own daily lives, as opposed to a situation in which their concerns are reflective of what others are facing. The data for this chapter are drawn from several sources, including time series data from Gallup beginning in the early 1950s as well as American National Election Study data from the past three decades that (broadly) match the time frame in which the objective situation in these four areas has become more insecure.

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