Relevant bibliographies by topics / Fashion forecasting

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fashion forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Fashion forecasting"

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Greenberg, Jerome. "Fashion forecasting." International Journal of Forecasting 5, no. 1 (January 1989): 144–45. http://dx.doi.org/10.1016/0169-2070(89)90079-4.

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Smith, Donna R. A. "Fashion forecasting." Journal of Retailing and Consumer Services 9, no. 6 (November 2002): 349–50. http://dx.doi.org/10.1016/s0969-6989(02)00034-6.

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Chiaroni, Keren Muriel. "Fashion and Design Trend-forecasting." International Journal of the Arts in Society: Annual Review 4, no. 4 (2009): 71–80. http://dx.doi.org/10.18848/1833-1866/cgp/v04i04/35670.

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Silva, Emmanuel, Hossein Hassani, Dag Madsen, and Liz Gee. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends." Social Sciences 8, no. 4 (April 4, 2019): 111. http://dx.doi.org/10.3390/socsci8040111.

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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.
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Bhuiyan Md Jashim Uddin, Kamran Muhammad, Uddin Md Nazim, and Razzaq Abdul. "Intellectual fashion forecasting simulations and application." World Journal of Advanced Research and Reviews 7, no. 1 (July 30, 2020): 133–41. http://dx.doi.org/10.30574/wjarr.2020.7.1.0231.

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Gaimster, Julia. "The changing landscape of fashion forecasting." International Journal of Fashion Design, Technology and Education 5, no. 3 (November 2012): 169–78. http://dx.doi.org/10.1080/17543266.2012.689014.

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Liu, Na, Shuyun Ren, Tsan-Ming Choi, Chi-Leung Hui, and Sau-Fun Ng. "Sales Forecasting for Fashion Retailing Service Industry: A Review." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/738675.

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Sales forecasting is crucial for many retail operations. It is especially critical for the fashion retailing service industry in which product demand is very volatile and product’s life cycle is short. This paper conducts a comprehensive literature review and selects a set of papers in the literature on fashion retail sales forecasting. The advantages and the drawbacks of different kinds of analytical methods for fashion retail sales forecasting are examined. The evolution of the respective forecasting methods over the past 15 years is revealed. Issues related to real-world applications of the fashion retail sales forecasting models and important future research directions are discussed.
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Lopes, Maria Vieira. "The discourse of fashion change: Trend forecasting in the fashion industry." Fashion, Style & Popular Culture 6, no. 3 (October 1, 2019): 333–49. http://dx.doi.org/10.1386/fspc.6.3.333_1.

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Wang, Lijing. "Discussion on Fashion Color Forecasting Researches for Textile and Fashion Industries." Journal of Fiber Bioengineering and Informatics 2, no. 1 (June 2009): 14–19. http://dx.doi.org/10.3993/jfbi06200902.

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Au, Kin-Fan, Tsan-Ming Choi, and Yong Yu. "Fashion retail forecasting by evolutionary neural networks." International Journal of Production Economics 114, no. 2 (August 2008): 615–30. http://dx.doi.org/10.1016/j.ijpe.2007.06.013.

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Dissertations / Theses on the topic "Fashion forecasting"

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McLuckie, Tracy Diane. "An investigation of colour forecasting." Thesis, De Montfort University, 2003. http://hdl.handle.net/2086/4101.

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Torlakcik, Tugce. "New Product Forecasting with Structured Analogy Method in the Fashion Industry : Case Studies with the Fashion Footwear Products." Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-166.

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Fashion and the contemporary environment as a whole, is a complex environment that requires retailers and wholesalers to adapt to the changes that constantly occurring. This adaptation is in a bid to ensure that more profits than loses are realized by the company. For this reason, companies have to use various methods to determine the best ways to improve their products. Companies resolve to introduction of new product to the market but the acceptance of new products to the fashion industry is not an assured factor but rather a gamble. This is mainly because of the industry’s characteristics. The main aim of this thesis is to analyze the methods that may be used to improve the accuracy of new products. The fashion industry has characteristics that may be considered as challenges because for instance, when a product is launched, one has to determine whether it is by a reputable designer or whether it is a trend, and with the fashion industry, trends are mainly turned into such by celebrities who introduce a certain design to the world for adoption. These challenges or characteristics are carefully analyzed and examined with the necessity of the introduction of new products analyzed. Data collection, being the main backbone of this thesis and multiple-case study method, is used to answer the research question as “How can structured analogy method be used to improve the forecast accuracy for the footwear products in the fashion industry “.Samples for case study have been chosen from footwear category. Structured analogy method is used to determine the accuracy of the information gathered from literature review.
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KREMER, VALERIE JACOBS. "TOWARDS EXPANDING A METHODOLOGY: UTILIZING SCENARIO PLANNING IN FASHION FORECASTING." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1022782166.

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Kang, Keang-Young. "Development of an Assortment Planning Model for Fashion Sensitive Products." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/26923.

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The purpose of this research is to develop an established assortment-planning model identifying procedures and activities for women's wear retail buyers. This research built three assortment-planning models: (a) a conceptual moddel based on a secondary data analysis, (b) a practical-use model based on interviews using questionnaire and a set of activity cards, (c) the suggested model based on the conncetion analysis of the previous two models.
Ph. D.
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Martinsson, Johan, and Johan Stighagen. "What to do when forecasting seems out of fashion? : A study on a fast growing fashion company." Thesis, Jönköping University, JIBS, EMM (Entrepreneurship, Marketing, Management), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-982.

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Problem: The Fashion Company (TFC) is a Swedish fast growing Fashion Company with suppliers and customers all over the world. Until now, TFC has kept up a reputation of a reliable distribution process to customers in which delivery dates are continuously met. Tradition-ally, the company has relied upon an early forecast as a part of their planning process. In-accuracy in that forecast leads to implications for the ordering process towards suppliers, which so far luckily have been manageable. However, the forecast seems to follow a trend of more and more inaccuracy for each season. If this trend continues, TFC are reluctantly aware of that the problem will affect their ability to fulfill customer delivery promises and damage their reputation.

Purpose: The purpose of the paper is to investigate the forecasting process and problems and also the underlying conditions affecting this process.

Method: A qualitative method was chosen on the basis of the purpose. To get a deeper understand-ing of TFC and its supply chain and to identify the main problem area, a pilot study was used prior to the main study. Mainly personal semi-structured interviews have been con-ducted. Email conversations have been a complementary to the personal interviews. The respondents from TFC were four people from the logistic department and one from sales department.

Conclusion: TFC’s current forecasting practice can be improved. However, as the nature of fashion products in themselves are very hard to predict TFC’s main problem will not be solved by continuously depending on accurate forecasting. Instead dependency on forecasting should be decreased by focusing on cutting lead times or reaching more flexible terms with suppliers. By both improving forecasting accuracy, in accordance with recommendations proposed in this study, and at the same time re-considering and upgrading the role of lead times and flexibility as factors in the supplier selection process, TFC can minimize their experienced problem.

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Di, Pierro Matteo <1990&gt. "Demand forecasting: a case study in the Italian fashion-retail industry." Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/8952.

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This master thesis is a case study that treats the application of different forecasting methodologies employed in the fashion retail industry. An Italian fashion company commisioned this preliminary study aiming to build a decisions support system for the Sales & Operations Manager. The work is divided in two parts, where a review of the available solutions provides the foundation for the application in the real world case. At the end, the results showed that demand forecasting systems provide a real business value as a tool to support the planning and restocking decisions.
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Frohm, Pauline, and Kara Xenia Tucholke. "The trend forecasting paradox? : An exploratory study of the compatibility of trend forecasting and sustainability." Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-23475.

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Trend forecasting is perceived to be an essential service for fashion companies to use in order to stay competitive in the fast-paced fashion industry. Yet, in times of climate change, appointing new trends each season is a questioned practice. Since trend forecasting aligns with the inherent obsolescence of fashion’s constant change, forecasting seems to stand in paradox with the imperatives of sustainability. Thus, this thesis aims to explore the role of trend forecasting to understand its compatibility with environmental sustainability. The review of previous research depicts the evolution of the trend forecasting field and displays prominent literature within fashion and sustainability, which together displays an apparent research gap that this study aims to fill. The thesis follows an exploratory design pursuing a multiple case study strategy applied through eight semi-structured interviews with trend forecasters and a content analysis of WGSN online trend forecasts. Findings of this study validate the existence of a trend forecasting paradox while also demonstrating areas of compatibilities. Customized forecasting and long-term approaches were concluded as compatible practices and may be integrated into both long-term and seasonal forecasting. This study also recognizes a need to differ between forecasting sustainability and sustainable forecasting. This thesis is believed contribute to an under-researched area and aid the trend forecasting industry to realize its impact on sustainability, as well as suggesting approaches on how to further incorporate sustainable practices into their work.
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King, Julie. "Colour forecasting : an investigation into how its development and use impacts on accuracy." Thesis, University of the Arts London, 2011. http://ualresearchonline.arts.ac.uk/5657/.

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Colour forecasting is a sector of trend forecasting which is arguably the most important link in the product development process, yet little is known about it, the methodology behind its development or its accuracy. It is part of a global trend forecasting industry valued recently at $36bn, providing information which is developed commercially eighteen months to two years ahead of the season. Used throughout the garment supply chain, by the yarn and fibre manufacturers, the fabric mills, garment designers and retailers, it plays a pivotal role in the fashion and textile industry, but appears in many different forms. Colour forecasts were first commercially produced in 1917, but became more widely used during the 1970s, and in recent years digital versions of colour forecasts have become increasingly popular. The investigation aimed to establish the historical background of the industry, mindful of the considerable changes to fashion manufacturing and retailing in recent decades. For the purposes of the investigation, a period spanning 25 years was selected, from 1985 to 2010. In reviewing the available literature, and the methodologies currently used in developing forecasting information, it became clear that there was a view that the process is very intuitive, and thus a lack of in depth academic literature. This necessitated a considerable quantity of primary research in order to fill the gaps in the knowledge regarding the development, use and accuracy of colour forecasting. A mixed method approach to primary research was required to answer the aim of the thesis, namely to investigate how colour forecasts are compiled, and examine their use, influence and accuracy within the fashion and textiles industry, suggesting methods for developing more accurate forecasts in the future. Interviews were conducted with industry practitioners comprising forecasters, designers and retailers to better understand how colour was developed and used within industry. Two longitudinal studies were carried out with the two largest UK clothing retailers to map their development and use of colour palettes, and understand better how colour contributes to the critical path and supply chain. Two colour development meetings were observed, one with a commercial colour forecaster, the other with an industry association, and two colour archives were studied to establish whether or not any identifiable and predictable colour cycles existed. Data from the interviews and longitudinal studies were analysed using a grounded approach, and revealed some new insights into the influences upon the development of colour forecasts both commercially and from the retailer's perspective. The sell through rates of merchandise, EPOS analysis and range of practices between those interviewed and the two retailers studied provided an interesting insight into working practices and how colour forecasting information is changed when used by the retailers. It was found that a group of core colours existed, which were used season after season, and consistently demonstrated a high sell through rate, such as black, white, grey and navy. In order to establish whether or not colour cycles were consistently predictable in their repetition, two colour forecasting archives were assessed. If predictable colour cycles existed, they would be a useful tool in developing more accurate forecasts. Unfortunately this was not the case, as no clear colour cycles were found. However, the archive, together with evidence from the retailers demonstrated the 'lifecycle' of fashion colours was longer than expected, as they took time to phase in and out. It was concluded that in general the less fashion led brands used their own signature colours and were able to develop colour palettes far later in the product development timeline. This approach could be adopted more widely by retailers and designers as it was discovered that although accuracy rates for colour forecasts are generally accepted to be around 80%, the commercial forecasters provide colour update cards closer to the season where at least 40% of the colours are changed. Very early information, two years ahead of the season is no longer necessary in the contemporary fashion and textiles industry.
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Alfredsson, Johan, and Lina Augustsson. "The Next Wave of the Suit-Era : A Forecasting Model of the Men’s Suit." Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-12731.

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Background   By the beginning of the 20th century, the men’s suit entered the menswear market as one the most important fashion garments everdevised. At the same time, fashion became mainly a female engagement, resulting in an under representation of men’s fashion through out the past decade. Relating to the textile and apparel industry, fashion forecasting has become an increasingly important business activity. But the nature of fashion forecasting and the historical neglecting of the men’s suit has created complications when performing this activity. Purpose   The purpose of this thesis is to examine the men’s suit and its development from the given starting point in the 20th century until today, in order to derive a fashion forecasting model suggesting its development by 2029. Design/methodology/approach   This thesis uses an abductive research approach and qualitative multi-methods to answer the research questions. The usage of an intermediate research project answers the first research question. The second research question is answered through the synthesis ofa literature study and semi-structured interviews. The third research question is answered through the derived forecasting model, accomplished through theory matching. Findings   By carrying out a historical investigation of the men’s suit, and then applying this to the derived forecasting model, the men’s suit is expected to be found in both single- and double-breast styles. The suit will have classical features represented through the length, canvas structure, and shoulder construction. Originality/value   This paper carries out a historical investigation of the men’s suit never been done before. It introduces an evaluation framework to categorise and classify the men’s suit, as well as a forecasting model followed by an actual fashion forecast.
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Moschopoulos, Theodosios, and Sofia Dahlström. "Colour Forecasting and its managerial implications." Thesis, Högskolan i Borås, Institutionen Textilhögskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-16778.

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In this thesis we examine the colour forecasting process, its methodology and how it is communicated and used in fashion companies. The study is foremost based on qualitative research and on semi-structured interviews with people within the forecasting industry. We have divided the data collection process that constitutes the basis of the actual forecast into steps, which consist of gathering both objective facts and more soft, subjective experiences. After having collected the data, colour forecasters start their analysis by breaking them down into thematical categories that depict specific patterns (themes). We have identified colour expertise, intuition, creativity and inspiration as the factors that help the forecaster interpret those patterns. The final forecasted colour stories are being presented in different media and contexts. Besides design style, market, customer base and lead-time, it is foremost the differentmanagement philosophies of either building creative, solid collections or fast fashion that define how to use the colour forecasting material. To help the reader understand the process we have constructed a model (aDaMas).
Program: Master in Fashion Management with specialisation in Fashion Marketing and Retailing
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Books on the topic "Fashion forecasting"

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McKelvey, Kathryn. Fashion Forecasting. New York: John Wiley & Sons, Ltd., 2009.

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Brannon, Evelyn L. Fashion forecasting. New York: Fairchild Publications, 2000.

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Lucas, Michael. Fashion forecasting. Derby: Derbyshire College of Higher Education, 1989.

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Janine, Munslow, ed. Fashion forecasting. Chichester, West Sussex, UK: Blackwell Pub., 2008.

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Fashion forecasting. 3rd ed. New York: Fairchild Books, 2010.

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Choi, Tsan-Ming, Chi-Leung Hui, and Yong Yu, eds. Intelligent Fashion Forecasting Systems: Models and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-39869-8.

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Perna, Rita. Fashion forecasting: A mystery or a method?. New York: Fairchild, 2001.

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Fashion forecasting: A mystery or a method? New York: Fairchild Publications, 1987.

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Bent, James. Asian street fashion. London: Thames & Hudson Ltd., 2014.

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Absatzprognosen für modeabhängige Unternehmen: Ein zweistufiges Absatzprognosesystem für Unternehmen mit modisch orientiertem Sortiment. Frankfurt am Main: P. Lang, 1993.

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Book chapters on the topic "Fashion forecasting"

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Choi, Tsan-Ming, Chi-Leung Hui, and Yong Yu. "Introduction: Intelligent Fashion Forecasting." In Intelligent Fashion Forecasting Systems: Models and Applications, 3–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39869-8_1.

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Luce, Leanne. "Deep Learning and Demand Forecasting." In Artificial Intelligence for Fashion, 155–66. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3931-5_10.

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Luce, Leanne. "Data Mining and Trend Forecasting." In Artificial Intelligence for Fashion, 141–51. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3931-5_9.

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Haensel, Alwin. "Forecasting Fashion Store Reservations:Booking Horizon Forecasting with Dynamic Updating." In Intelligent Fashion Forecasting Systems: Models and Applications, 95–120. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39869-8_6.

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Muttaraju, Chakita, Ramya Narasimha Prabhu, S. Sheetal, D. Uma, and S. S. Shylaja. "IFF: An Intelligent Fashion Forecasting System." In Computational Intelligence and Data Analytics, 487–98. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3391-2_37.

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Yelland, Phillip M., and Xiaojing Dong. "Forecasting Demand for Fashion Goods:A Hierarchical Bayesian Approach." In Intelligent Fashion Forecasting Systems: Models and Applications, 71–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39869-8_5.

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Cramer, Jo. "Use Forecasting: Designing Fashion Garments for Extended Use." In Sustainable Design in Textiles and Fashion, 85–104. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2466-7_5.

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Kaya, Murat, Engin Yeşil, M. Furkan Dodurka, and Sarven Sıradağ. "Fuzzy Forecast Combining for Apparel Demand Forecasting." In Intelligent Fashion Forecasting Systems: Models and Applications, 123–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39869-8_7.

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Thomassey, Sébastien. "Sales Forecasting in Apparel and Fashion Industry: A Review." In Intelligent Fashion Forecasting Systems: Models and Applications, 9–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39869-8_2.

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Yu, Yong, Sau-Fun Ng, Chi-Leung Hui, Na Liu, and Tsan-Ming Choi. "Intelligent Fashion Colour Trend Forecasting Schemes: A Comparative Study." In Intelligent Fashion Forecasting Systems: Models and Applications, 147–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39869-8_8.

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Conference papers on the topic "Fashion forecasting"

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Al-Halah, Ziad, Rainer Stiefelhagen, and Kristen Grauman. "Fashion Forward: Forecasting Visual Style in Fashion." In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017. http://dx.doi.org/10.1109/iccv.2017.50.

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Ma, Yunshan, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, and Tat-Seng Chua. "Knowledge Enhanced Neural Fashion Trend Forecasting." In ICMR '20: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3372278.3390677.

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Bruzzone, Agostino, Fransco Longo, Letizia Nicoletti, Alessandro Chiurco, and Christian Bartolucci. "Multiple Forecasting Algorithms for Demand Forecasting in the Fashion Industry." In 2013 8th EUROSIM Congress on Modelling and Simulation (EUROSIM). IEEE, 2013. http://dx.doi.org/10.1109/eurosim.2013.122.

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Zhao, Li, Muzhen Li, and Peng Sun. "Neo-Fashion: A Data-Driven Fashion Trend Forecasting System using Machine Learning through Catwalk Analysis." In Pivoting for the Pandemic. Iowa State University Digital Press, 2020. http://dx.doi.org/10.31274/itaa.12062.

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Gu, Wen, and Xiaogang Liu. "System of Color Resource Management for Fashion Color Forecasting." In 2010 International Conference on Management and Service Science (MASS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icmss.2010.5577096.

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Yesil, Engin, Murat Kaya, and Sarven Siradag. "Fuzzy forecast combiner design for fast fashion demand forecasting." In 2012 International Symposium on Innovations in Intelligent Systems and Applications (INISTA). IEEE, 2012. http://dx.doi.org/10.1109/inista.2012.6247034.

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Speight, LaParia, and Elena Karpova. "Analysis of Fashion Trend Forecasting Industry: Challenges, Opportunities, and Outlooks." In Innovate to Elevate. Iowa State University Digital Press, 2022. http://dx.doi.org/10.31274/itaa.15872.

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Maleku Shrestha, R., G. Craparotta, S. Thomassey, and R. Moore. "A two staged forecasting scheme considering the constraints of sales forecasting in the fashion industry." In Conference on Data Science and Knowledge Engineering for Sensing Decision Support (FLINS 2018). WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813273238_0187.

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Choi, Tsan-Ming, Chi-Leung Hui, and Yong Yu. "Intelligent time series fast forecasting for fashion sales: A research agenda." In 2011 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2011. http://dx.doi.org/10.1109/icmlc.2011.6016870.

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Bruzzone, Agostino G., Marina Massei, and Luca Pierfederici. "Forecasting models for non continuative production systems application in fashion industry." In the 2010 Spring Simulation Multiconference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1878537.1878600.

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Reports on the topic "Fashion forecasting"

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Adomaitis, Alyssa Dana. A Fashion Paradox: Reflective Thinking for Fashion Forecasting. Ames: Iowa State University, Digital Repository, 2013. http://dx.doi.org/10.31274/itaa_proceedings-180814-750.

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Fowler, Deborah C. Pinterest and fashion forecasting: Engaging students in the learning process. Ames: Iowa State University, Digital Repository, 2013. http://dx.doi.org/10.31274/itaa_proceedings-180814-759.

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Fiore, Ann Marie Marie, and Danielle S. Testa. A Fashion Trend Forecasting Course as a Gateway to Career Discovery. Ames (Iowa): Iowa State University. Library, January 2019. http://dx.doi.org/10.31274/itaa.8470.

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DuBreuil, Mikayla, and Sheng Lu. Traditional Versus Big-Data Based Fashion Trend Forecasting: An Examination Using WGSN and EDITED. Ames (Iowa): Iowa State University. Library, January 2019. http://dx.doi.org/10.31274/itaa.8246.

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You might also be interested in the extended bibliographies on the topic 'Fashion forecasting' for particular source types:
Journal articles Dissertations / Theses Books
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