The time-series forecasting makes a substantial contribution in timely decision-making. In
this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) …

With the rapid growth in the availability of data and computational technologies, multiple
machine learning frameworks have been proposed for forecasting air pollution. However …

Due to the development of transportation, population growth and industrial activities, air
quality has become a major issue in urban areas. Poor air quality leads to rising health …

Time-series prediction technology plays a significant role in evaluating the health status of
power transformers and forecasting inchoate operation failure. This study presents a …

Accurate prediction of cutter head torque is of great significance to ensure the safe tunneling
of the shield tunneling machine. However, because geological conditions are complex and …

This study discusses a hybrid model of Singular Spectrum Analysis (SSA) with
Autoregressive Integrated Moving Average (ARIMA) and Fuzzy Time Series (FTS) for …

We propose a general method for producing reliable short‐term point and interval forecasts
of daily maximum tropospheric ozone concentrations, a time series with a significant …

High amounts of air pollution in crowded urban areas are always considered as one of the
major environmental challenges especially in developing countries. Despite the errors in air …

Abstract The Singular Spectrum Analysis (SSA)-Autoregressive Integrated Moving Average
(ARIMA) hybrid method is a good combination of forecasting methods to improve forecasting …

This thesis develops practical Bayesian estimators and exploration methods for count data
collected by autonomous robots with unreliable sensors for long periods of time. It …