22 records from EconBiz based on author Name

1. Digital humans in fashion : will consumers interact?

Silva, Emmanuel Sirimal; Bonetti, Francesca;
2021
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
Citations: 43 (based on OpenCitations) Cited By

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2. The science of statistics versus data science : what is the future?

Hassani, Hossein; Beneki, Christina; Silva, Emmanuel Sirimal; Vandeput, Nicolas; Madsen, Dag Øivind;
2021
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
Citations: 4 (based on OpenCitations) Cited By

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3. UK mid-market department stores : is fashion product assortment one key to regaining competitive advantage?

Donnelly, Shannon; Gee, Liz; Silva, Emmanuel Sirimal;
2020
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
Citations: 14 (based on OpenCitations) Cited By

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4. The application of big data in fashion retailing : a narrative review

Madsen, Dag Øivind; Silva, Emmanuel Sirimal; Hassani, Hossein;
2020
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link Link

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5. Fusing Big Data, Blockchain and Cryptocurrency : Their Individual and Combined Importance in the Digital Economy

abstract

1. Introduction -- 2. Big Data and Blockchain -- 3. Blockchain and Cryptocurrency -- 4. Big Data and Cryptocurrency -- 5. Fusing Big Data, Blockchain and Cryptocurrency.

Hassani, Hossein; Huang, Xu; Silva, Emmanuel Sirimal;
2019
Availability: Link
Citations: 6 (based on OpenCitations) Cited By

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6. Fusing big data, blockchain and cryptocurrency : their individual and combined importance in the digital economy

abstract

1. Introduction -- 2. Big Data and Blockchain -- 3. Blockchain and Cryptocurrency -- 4. Big Data and Cryptocurrency -- 5. Fusing Big Data, Blockchain and Cryptocurrency.

Hassani, Hossein; Huang, Xu; Silva, Emmanuel Sirimal;
2019
Availability: Link
Citations: 6 (based on OpenCitations) Cited By

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7. Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis

Hassani, Hossein; Rua, António; Silva, Emmanuel Sirimal; Thomakos, Dimitrios D.;
2019
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
Citations: 16 (based on OpenCitations) Cited By

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8. Big data : a big opportunity for the petroleum and petrochemical industry

Hassani, Hossein; Silva, Emmanuel Sirimal;
2018
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link Link
Citations: 36 (based on OpenCitations) Cited By

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9. Forecasting UK consumer price inflation using inflation forecasts

Hassani, Hossein; Silva, Emmanuel Sirimal;
2018
Type: Aufsatz in Zeitschrift; Article in journal;
Availability: Link
Citations: 11 (based on OpenCitations) Cited By

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10. Googling Fashion : Forecasting Fashion Consumer Behaviour Using Google Trends

abstract

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 techniques to determine the best univariate forecasting model for “Burberry” Google Trends. In addition, we also introduce singular spectrum analysis as a useful tool for denoising fashion consumer Google Trends and apply a recently developed hybrid neural network model to generate forecasts. Our initial results indicate that there is no single univariate model (out of ARIMA, exponential smoothing, TBATS, and neural network autoregression) that can provide the best forecast of fashion consumer Google Trends for Burberry across all horizons. In fact, we find neural network autoregression (NNAR) to be the worst contender. We then seek to improve the accuracy of NNAR forecasts for fashion consumer Google Trends via the introduction of singular spectrum analysis for noise reduction in fashion data. The hybrid neural network model (Denoised NNAR) succeeds in outperforming all competing models across all horizons, with a majority of statistically significant outcomes at providing the best forecast for Burberry's highly seasonal fashion consumer Google Trends. In an era of big data, we show the usefulness of Google Trends, denoising and forecasting consumer behaviour for the fashion industry

Silva, Emmanuel Sirimal; Hassani, Hossein; Madsen, Dag Øivind; Gee, Liz;
2020
Availability: Link

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