Real-time data-driven missing data imputation for short-term sensor data of marine systems. A comparative study
In the maritime industry, sensors are utilised to implement condition-based maintenance
(CBM) to assist decision-making processes for energy efficient operations of marine …
(CBM) to assist decision-making processes for energy efficient operations of marine …
ForecastTB—An R package as a test-bench for time series forecasting—Application of wind speed and solar radiation modeling
This paper introduces an R package ForecastTB that can be used to compare the accuracy
of different forecasting methods as related to the characteristics of a time series dataset. The …
of different forecasting methods as related to the characteristics of a time series dataset. The …
A novel imputation methodology for time series based on pattern sequence forecasting
Abstract The Pattern Sequence Forecasting (PSF) algorithm is a previously described
algorithm that identifies patterns in time series data and forecasts values using periodic …
algorithm that identifies patterns in time series data and forecasts values using periodic …
[HTML][HTML] cleanTS: Automated (AutoML) tool to clean univariate time series at microscales
Data cleaning is one of the most important tasks in data analysis processes. One of the
perennial challenges in data analytics is the detection and handling of non-valid data …
perennial challenges in data analytics is the detection and handling of non-valid data …
Forecasting COVID-19 impact in India using pandemic waves Nonlinear Growth Models
The ongoing pandemic of the coronavirus disease 2019 (COVID-19) started in China and
devastated a vast majority of countries. In India, COVID-19 cases are steadily increasing …
devastated a vast majority of countries. In India, COVID-19 cases are steadily increasing …
Quantitative evaluation of imputation methods using bounds estimation of the coefficient of determination for data-driven models with an application to drilling logs
With the constantly increasing quantity of data recorded in the oil and gas industry, data
analytics and data-driven algorithms are gaining popularity. Meanwhile, they are highly …
analytics and data-driven algorithms are gaining popularity. Meanwhile, they are highly …
Comparison of missing data infilling mechanisms for recovering a real-world single station streamflow observation
Reconstructing missing streamflow data can be challenging when additional data are not
available, and missing data imputation of real-world datasets to investigate how to ascertain …
available, and missing data imputation of real-world datasets to investigate how to ascertain …
Wind turbine power curves based on the weibull cumulative distribution function
The representation of a wind turbine power curve by means of the cumulative distribution
function of a Weibull distribution is investigated in this paper, after having observed the …
function of a Weibull distribution is investigated in this paper, after having observed the …
[HTML][HTML] Assessing methods for multiple imputation of systematic missing data in marine fisheries time series with a new validation algorithm
Time series from fisheries often contain multiple missing data. This is a severe limitation that
prevents using the data for research on population dynamics, stock assessment, forecasting …
prevents using the data for research on population dynamics, stock assessment, forecasting …
Impacts of land use changes on discharge and water quality in rivers and streams: Case study of the continental United States
C Gunawardana, W McDonald - JAWRA Journal of the …, 2024 - Wiley Online Library
Water quality trends in streams and rivers are impacted by several factors including land use
of the watershed; however, it is unclear what influence changes in the land use of a …
of the watershed; however, it is unclear what influence changes in the land use of a …