Emmanuel Sirimal Silva
Biographic Data
| ID | 3937242 |
|---|---|
| NAME | Emmanuel Sirimal Silva |
| GIVEN NAMES | Emmanuel Sirimal |
| FAMILY NAME | Silva |
| SIGNATURE | SILVA E S |
| AFFILIATIONS | University of the Arts London |
| ORCID | 0000-0003-3851-9230 |
| VERIFIED | Yes |
| TOTAL WORKS | 10 |
| TOTAL CITATIONS | 53 |
| AUTHOR COUNT | 10 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 4 |
Understanding the psychology of fashion
Fashion is one of the most common (aesthetic) activities, yet aside from a select number of works, systematic studies of clothing preference remain relatively rare. This study aims to extend this line of research by offering a more generalizable understanding of the predictors and descriptions of everyday clothing preferences. Samples were drawn from two English-speaking cultures (i.e., the UK and the USA; Ns = 402 and 400 respectively) and a ran…
Forecasting tourism growth with State-Dependent Models
We introduce two forecasting methods based on a general class of non-linear models called ‘State-Dependent Models’ (SDMs) for tourism demand forecasting. Using a Monte Carlo simulation which generated data from linear and non-linear models, we evidence how estimations from SDMs can capture the level shifts pattern and nonlinearity in data. Next, we apply two new forecasting methods based on SDMs to forecast tourism demand growth in Japan. The for…
Modelling’ UK tourism demand using fashion retail sales
The science of statistics versus data science
Forecasting changes of economic inequality
We use a boosting algorithm to forecast changes in three income- and three consumption-based inequality measures. Unlike the existing literature, which basically deals with in-sample predictability, we analyze the role of large number of predictors in out-of-sample prediction of inequality growth. Further, deviating from the annual data-based literature on inequality, we study quarterly UK data covering the period from 1975Q1 to 2016Q1. We find t…
Forecasting tourism demand with denoised neural networks
Googling Fashion
This paper aims to discuss the current state of Google Trends as a useful tool for fashion consumer analytics, show the importance of being able to forecast fashion consumer trends and then presents a univariate forecast evaluation of fashion consumer Google Trends to motivate more academic research in this subject area. Using Burberry-a British luxury fashion house-as an example, we compare several parametric and nonparametric forecasting techni…
Forecasting accuracy evaluation of tourist arrivals
Cross country relations in European tourist arrivals
Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis
Forecasting accuracy evaluation of tourist arrivals
Forecasting tourism demand with denoised neural networks
Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis
Googling Fashion
This paper aims to discuss the current state of Google Trends as a useful tool for fashion consumer analytics, show the importance of being able to forecast fashion consumer trends and then presents a univariate forecast evaluation of fashion consumer Google Trends to motivate more academic research in this subject area. Using Burberry-a British luxury fashion house-as an example, we compare several parametric and nonparametric forecasting techni…
Cross country relations in European tourist arrivals
Forecasting tourism growth with State-Dependent Models
We introduce two forecasting methods based on a general class of non-linear models called ‘State-Dependent Models’ (SDMs) for tourism demand forecasting. Using a Monte Carlo simulation which generated data from linear and non-linear models, we evidence how estimations from SDMs can capture the level shifts pattern and nonlinearity in data. Next, we apply two new forecasting methods based on SDMs to forecast tourism demand growth in Japan. The for…
Forecasting changes of economic inequality
We use a boosting algorithm to forecast changes in three income- and three consumption-based inequality measures. Unlike the existing literature, which basically deals with in-sample predictability, we analyze the role of large number of predictors in out-of-sample prediction of inequality growth. Further, deviating from the annual data-based literature on inequality, we study quarterly UK data covering the period from 1975Q1 to 2016Q1. We find t…
Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis
Forecasting accuracy evaluation of tourist arrivals
Cross country relations in European tourist arrivals
Forecasting changes of economic inequality
We use a boosting algorithm to forecast changes in three income- and three consumption-based inequality measures. Unlike the existing literature, which basically deals with in-sample predictability, we analyze the role of large number of predictors in out-of-sample prediction of inequality growth. Further, deviating from the annual data-based literature on inequality, we study quarterly UK data covering the period from 1975Q1 to 2016Q1. We find t…
Forecasting tourism demand with denoised neural networks
Googling Fashion
This paper aims to discuss the current state of Google Trends as a useful tool for fashion consumer analytics, show the importance of being able to forecast fashion consumer trends and then presents a univariate forecast evaluation of fashion consumer Google Trends to motivate more academic research in this subject area. Using Burberry-a British luxury fashion house-as an example, we compare several parametric and nonparametric forecasting techni…
The science of statistics versus data science
Forecasting tourism growth with State-Dependent Models
We introduce two forecasting methods based on a general class of non-linear models called ‘State-Dependent Models’ (SDMs) for tourism demand forecasting. Using a Monte Carlo simulation which generated data from linear and non-linear models, we evidence how estimations from SDMs can capture the level shifts pattern and nonlinearity in data. Next, we apply two new forecasting methods based on SDMs to forecast tourism demand growth in Japan. The for…
Modelling’ UK tourism demand using fashion retail sales
Understanding the psychology of fashion
Fashion is one of the most common (aesthetic) activities, yet aside from a select number of works, systematic studies of clothing preference remain relatively rare. This study aims to extend this line of research by offering a more generalizable understanding of the predictors and descriptions of everyday clothing preferences. Samples were drawn from two English-speaking cultures (i.e., the UK and the USA; Ns = 402 and 400 respectively) and a ran…
Computer Science (7 works) · Econometrics (7 works) · Economics (7 works) · Geography (7 works) · Tourism (6 works) · Mathematics (5 works) · Statistics (5 works) · Artificial Intelligence (4 works) · Artificial neural network (4 works) · Autoregressive integrated moving average (4 works)