Modeling of lignocellulosic biomass pyrolysis processes can be used to determine their key
operating and design parameters. This requires significant amount of information about …

Research Methodology is meant to provide a broad guideline to facilitate and steer the
whole of a research activity in any discipline. With the ambit and amount of research …

This paper presents a novel study on harnessing Large Language Models'(LLMs)
outstanding knowledge and reasoning abilities for explainable financial time series …

The book describes the use of smoothing techniques in statistics, including both density
estimation and nonparametric regression. Considerable advances in research in this area …

Amongmanyexcitingdevelopmentsinstatistic…, nonlineartimeseriesanddata-
analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In …

Time series econometrics is a rapidly evolving field. Particularly, the cointegration revolution
has had a substantial impact on applied analysis. Hence, no textbook has managed to cover …

Although many of the models commonly used in empirical finance are linear, the nature of
financial data suggests that non-linear models are more appropriate for forecasting and …

In the last ten years, there has been increasing interest and activity in the general area of
partially linear regression smoothing in statistics. Many methods and techniques have been …

The local linear regression technique is applied to estimation of functional-coefficient
regression models for time series data. The models include threshold autoregressive …

This empirical paper compares the accuracy of 12 time series methods for short-term (day-
ahead) spot price forecasting in auction-type electricity markets. The methods considered …