Predicting streamflow in intermittent rivers and ephemeral streams (IRES), particularly those
in climate hotspots such as the headwaters of the Colorado River in Texas, is a necessity for …

Missing values in water level data is a persistent problem in data modelling and especially
common in developing countries. Data imputation has received considerable research …

The proposed framework consists of three modules as an outlier detection method for indoor
air quality data. We first use a long short-term memory autoencoder (LSTM-AE) based …

Statistical analyses often require unbiased and reliable data completion. In this work, we
imputed missing fine particulate matter (PM2. 5) observations from eight years (2012–2019) …

In recent years, climate change has demonstrated the volatility of unexpected events such
as typhoons, flooding, and tsunamis that affect people, ecosystems and economies. As a …

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 …

Single-Case Experimental Designs (SCEDs) are increasingly recognized as a valuable
alternative to group designs. Mediation analysis is useful in SCEDs contexts because it …

Background: The impact of missing data on individual continuous glucose monitoring (CGM)
data is unknown but can influence clinical decision-making for patients. Objective: We aimed …

The problem of missing data is frequently met in time series analysis. If not appropriately
addressed, it usually leads to failed modeling and distorted forecasting. To deal with high …

In this article, we analyze the energy consumption data of business customers registered by
trading companies in Poland. We focus on estimating missing data in hourly series, as …