scholarly journals An Innovative Concept for a Multivariate Plausibility Assessment of Simultaneously Recorded Data

Animals ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1412
Author(s):  
André Mensching ◽  
Marleen Zschiesche ◽  
Jürgen Hummel ◽  
Armin Otto Schmitt ◽  
Clément Grelet ◽  
...  
Keyword(s):  
Time Series Data ◽  
Extreme Values ◽  
Blood Parameters ◽  
Management Tool ◽  
Series Data ◽  
Complex Data ◽  
Climate Data ◽  
Data Set ◽  
Continuous Measurements ◽  
Recorded Data

The aim of this work was to develop an innovative multivariate plausibility assessment (MPA) algorithm in order to differentiate between ‘physiologically normal’, ‘physiologically extreme’ and ‘implausible’ observations in simultaneously recorded data. The underlying concept is based on the fact that different measurable parameters are often physiologically linked. If physiologically extreme observations occur due to disease, incident or hormonal cycles, usually more than one measurable trait is affected. In contrast, extreme values of a single trait are most likely implausible if all other traits show values in a normal range. For demonstration purposes, the MPA was applied on a time series data set which was collected on 100 cows in 10 commercial dairy farms. Continuous measurements comprised climate data, intra-reticular pH and temperature, jaw movement and locomotion behavior. Non-continuous measurements included milk yield, milk components, milk mid-infrared spectra and blood parameters. After the application of the MPA, in particular the pH data showed the most implausible observations with approximately 5% of the measured values. The other traits showed implausible values up to 2.5%. The MPA showed the ability to improve the data quality for downstream analyses by detecting implausible observations and to discover physiologically extreme conditions even within complex data structures. At this stage, the MPA is not a fully developed and validated management tool, but rather corresponds to a basic concept for future works, which can be extended and modified as required.

scholarly journals Evaluating the Performance of Multiple Imputation Methods for Handling Missing Values in Time Series Data: A Study Focused on East Africa, Soil-Carbonate-Stable Isotope Data

Stats ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 457-467 ◽  
Author(s):  
Hossein Hassani ◽  
Mahdi Kalantari ◽  
Zara Ghodsi
Keyword(s):  
Time Series ◽  
East Africa ◽  
Quantitative Research ◽  
Missing Values ◽  
Time Series Data ◽  
Extreme Values ◽  
Singular Spectrum Analysis ◽  
Series Data ◽  
Data Set ◽  
Imputation Methods

In all fields of quantitative research, analysing data with missing values is an excruciating challenge. It should be no surprise that given the fragmentary nature of fossil records, the presence of missing values in geographical databases is unavoidable. As in such studies ignoring missing values may result in biased estimations or invalid conclusions, adopting a reliable imputation method should be regarded as an essential consideration. In this study, the performance of singular spectrum analysis (SSA) based on L 1 norm was evaluated on the compiled δ 13 C data from East Africa soil carbonates, which is a world targeted historical geology data set. Results were compared with ten traditionally well-known imputation methods showing L 1 -SSA performs well in keeping the variability of the time series and providing estimations which are less affected by extreme values, suggesting the method introduced here deserves further consideration in practice.

Producing Daily Landsat Snow Cover Time-Series Data

2021 ◽  
Author(s):  
Fatemeh Zakeri ◽  
Gregoire Mariethoz
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Snow Cover ◽  
Time Series Data ◽  
Landsat Imagery ◽  
Swiss Alps ◽  
Series Data ◽  
Landsat Data ◽  
Climate Data ◽  
Data Set

<p>Snow cover maps are critical for hydrological studies as well as climate change impacts assessment. Remote sensing plays a vital role in providing snow cover information. However, acquisition limitations such as clouds, shadows, or revisiting time limit accessing daily complete snow cover maps obtained from remote sensing. This study explores the generation of synthetic daily Landsat time-series data focusing on snow cover using available Landsat data and climate data for 2020 in the Western Swiss Alps (Switzerland). <br>Landsat surface reflectance is predicted using all available Landsat imagery from 1984 to2020 and ERA5 reanalysis precipitation and air temperature daily data in this study. For a given day where there is no Landsat data, the proposed procedure computes a similarity metric to find a set of days having a similar climatic pattern and for which satellite data is available. These best match images constitute possible snow cover scenarios on the target day and can be used as stochastic input to impact models. <br>Visual comparison and quantitative assessment are used to evaluate the accuracy of the generated images. In both accuracy assessments, some real Landsat data are omitted from the searching data set, and synthetic images are compared visually with real Landsat images. In the quantitative evaluation, the RSME between the real and artificial images is computed in a cross-validation fashion. Both accuracy procedures demonstrate that the combination of Landsat and climate data can predict Landsat's daily reflectance focusing on snow cover.</p>

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.

Sonification and Animation of Multivariate Data to Illuminate Dynamics of Geyser Eruptions

2020 ◽  
Vol 44 (1) ◽  
pp. 35-50
Author(s):  
Anna Barth ◽  
Leif Karlstrom ◽  
Benjamin K. Holtzman ◽  
Arthur Paté ◽  
Avinash Nayak
Keyword(s):  
Time Series Data ◽  
Multivariate Data ◽  
Rapid Identification ◽  
Series Data ◽  
Complex Data ◽  
Learning Tools ◽  
Call And Response ◽  
Eruptive Cycle ◽  
Nonstationary Data ◽  
Auditory Systems

Abstract Sonification of time series data in natural science has gained increasing attention as an observational and educational tool. Sound is a direct representation for oscillatory data, but for most phenomena, less direct representational methods are necessary. Coupled with animated visual representations of the same data, the visual and auditory systems can work together to identify complex patterns quickly. We developed a multivariate data sonification and visualization approach to explore and convey patterns in a complex dynamic system, Lone Star Geyser in Yellowstone National Park. This geyser has erupted regularly for at least 100 years, with remarkable consistency in the interval between eruptions (three hours) but with significant variations in smaller scale patterns between each eruptive cycle. From a scientific standpoint, the ability to hear structures evolving over time in multiparameter data permits the rapid identification of relationships that might otherwise be overlooked or require significant processing to find. The human auditory system is adept at physical interpretation of call-and-response or causality in polyphonic sounds. Methods developed here for oscillatory and nonstationary data have great potential as scientific observational and educational tools, for data-driven composition with scientific and artistic intent, and towards the development of machine learning tools for pattern identification in complex data.

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 Climate, snow, and soil moisture data set for the Tuolumne and Merced river watersheds, California, USA

2019 ◽  
Vol 11 (1) ◽  
pp. 101-110 ◽  
Author(s):  
James W. Roche ◽  
Robert Rice ◽  
Xiande Meng ◽  
Daniel R. Cayan ◽  
Michael D. Dettinger ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Sierra Nevada ◽  
Land Surface ◽  
Forest Canopy ◽  
Snow Water Equivalent ◽  
Process Models ◽  
Series Data ◽  
Climate Data ◽  
Data Set ◽  
Data Repositories

Abstract. We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010–2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (Level 0), data that are serially continuous with noise and nonphysical points removed (Level 1), and, where possible, data that are gap filled using linear interpolation or regression with a nearby station record (Level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow depth and co-located soil moisture measurements (2–6) from four locations spanning the rain–snow transition zone in the center of the domain. Spatial data layers pertinent to snowpack modeling in this data set are basin polygons and 100 m resolution rasters of elevation, vegetation type, forest canopy cover, tree height, transmissivity, and extinction coefficient. All data are available from online data repositories (https://doi.org/10.6071/M3FH3D).

Economic growth, Industrialization, Trade, Electricity production and Carbon dioxide emissions: Evidence from Ghana

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Kingsley Appiah ◽  
Rhoda Appah ◽  
Oware Kofi Mintah ◽  
Benjamin Yeboah
Keyword(s):  
Economic Growth ◽  
Carbon Dioxide ◽  
Carbon Dioxide Emissions ◽  
Time Series Data ◽  
Electricity Production ◽  
Series Data ◽  
Data Set ◽  
Development Indicators ◽  
Explanatory Variables ◽  
Distributed Lag

Abstract: The study scrutinized correlation between electricity production, trade, economic growth, industrialization and carbon dioxide emissions in Ghana. Our study disaggregated trade into export and import to spell out distinctive and individual variable contribution to emissions in Ghana. In an attempt to investigate, the study used time-series data set of World Development Indicators from 1971 to 2014. By means of Autoregressive Distributed Lag (ARDL) cointegrating technique, study established that variables are co-integrated and have long-run equilibrium relationship. Results of long-term effect of explanatory variables on carbon dioxide emissions indicated that 1% each increase of economic growth and industrialization, will cause an increase of emissions by 16.9% and 79% individually whiles each increase of 1% of electricity production, trade exports, trade imports, will cause a decrease in carbon dioxide emissions by 80.3%, 27.7% and 4.1% correspondingly. In the pursuit of carbon emissions' mitigation and achievement of Sustainable Development Goal (SDG) 13, Ghana need to increase electricity production and trade exports.   

Sign in / Sign up

Export Citation Format

Share Document