Journal articles: '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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Saidan, Suriati, Husna Saaidin, Wan Nadhra Ixora Wan Kamarulbaharin, Norzaleha Zainun, and Mohd Hafnidzam Adzmi. "Muslimah Design Trends Through The Role Of Fashion Forecasting." Idealogy Journal 7, no. 1 (April 1, 2022): 31–40. http://dx.doi.org/10.24191/idealogy.v7i1.331.

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Muslimah fashion design nowadays is a fashion trend that is the best alternative for Muslim women who want to cover their aurat with an attractive style. With a variety of options, Islamic clothing is not considered conservative or outdated. Therefore the form of fashion design should be more contemporary and in accordance with Islamic characteristics and suitable for use by all nations. In this paper the researcher will look at the fashion forecasting process used in the production of Muslimah clothing. Fashion trends are an important element in determining the concept of clothing design. As a trend forecasting concept, several things have a significant impact on the fashion industry. The ability to forecast trends in fashion, technology, and culture is a critical area of the marketing industry dedicated to identifying patterns of consumer behavior while helping companies and brands connect with audiences. Fashion trends are styles of clothing and accessories that are popular at a particular time. It will affect the popularity and lifestyle for example through the use of colors and fabrics used. Fashion forecasters will do research somewhere to find out new trends and try to bring some new ideas about the brand. It requires scientific skills and creative concepts. Thus, fashion forecasting is a field in the fashion industry that is concerned with predicting upcoming fashion trends in terms of colors, design techniques, textile materials, and more that lead to consumer demand. Fashion forecasters produce trend reports that are used to develop a brand for the production of a product. In the process of making designs, designers need to pay attention to fashion predictions which in addition to having Islamic characteristics, the design can be comparable to international designs.

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Chen, I.-Fei, and Chi-Jie Lu. "Demand Forecasting for Multichannel Fashion Retailers by Integrating Clustering and Machine Learning Algorithms." Processes 9, no. 9 (September 3, 2021): 1578. http://dx.doi.org/10.3390/pr9091578.

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In today’s rapidly changing and highly competitive industrial environment, a new and emerging business model—fast fashion—has started a revolution in the apparel industry. Due to the lack of historical data, constantly changing fashion trends, and product demand uncertainty, accurate demand forecasting is an important and challenging task in the fashion industry. This study integrates k-means clustering (KM), extreme learning machines (ELMs), and support vector regression (SVR) to construct cluster-based KM-ELM and KM-SVR models for demand forecasting in the fashion industry using empirical demand data of physical and virtual channels of a case company to examine the applicability of proposed forecasting models. The research results showed that both the KM-ELM and KM-SVR models are superior to the simple ELM and SVR models. They have higher prediction accuracy, indicating that the integration of clustering analysis can help improve predictions. In addition, the KM-ELM model produces satisfactory results when performing demand forecasting on retailers both with and without physical stores. Compared with other prediction models, it can be the most suitable demand forecasting method for the fashion industry.

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Nenni, Maria Elena, Luca Giustiniano, and Luca Pirolo. "Demand Forecasting in the Fashion Industry: A Review." International Journal of Engineering Business Management 5 (January 1, 2013): 37. http://dx.doi.org/10.5772/56840.

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Forecasting demand is a crucial issue for driving efficient operations management plans. This is especially the case in the fashion industry, where demand uncertainty, lack of historical data and seasonal trends usually coexist. Many approaches to this issue have been proposed in the literature over the past few decades. In this paper, forecasting methods are compared with the aim of linking approaches to the market features.

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Kawinakrathiti, Komaek, Suphakant Phimoltares, and Patcha Utiswannakul. "Developing Forecasting Model in Thailand Fashion Market Based on Statistical Analysis and Content-Based Image Retrieval." International Journal of E-Entrepreneurship and Innovation 5, no. 1 (January 2015): 32–46. http://dx.doi.org/10.4018/ijeei.2015010103.

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Traditional trend forecasting process in Thailand fashion industry was challenged by a fast fashion. In this paper, the Content-Based Image Retrieval (CBIR) technique is utilized for retrieval of a fashion trendsetter in fast fashion influence. Firstly, six fashion theories were implemented as 12 variables affecting the trendsetter. Cluster analysis, and factor analysis approach were used to find out the source of a fashion trendsetter as well. Cluster analysis separated all samples into three groups with different fashion ways. Moreover, factor analysis technique grouped all variables into three important factors. From such techniques, Internet media clearly is the best source of a fashion trendsetter. In the authors' model, traditional forecasting sources were added up with a fast fashion influence from CBIR. Then, the CBIR was evaluated in terms of efficiency compared with a real fashion expert in the Thai fashion industry. From statistical test, spatial color distribution yields high efficiency in selecting similar fashion style as a fashion expert.

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Chen, Dali, Wenbiao Liang, Kelan Zhou, and Fan Liu. "Sales Forecasting for Fashion Products Considering Lost Sales." Applied Sciences 12, no. 14 (July 13, 2022): 7081. http://dx.doi.org/10.3390/app12147081.

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Sales forecasting for new products is significantly important for fashion retailer companies because prediction with high accuracy helps the company improve management efficiency and customer satisfaction. The low inventory strategy of fashion products and the low stock level in each brick-and-mortar store lead to serious censored demand problems, making forecasting difficult. In this regard, a two layers (TLs) model is proposed in this paper to predict the total sales of new products. In the first layer, the demand is estimated by linear regression (LR). In the second layer, sales are modeled as a function of not only the demand but also the inventory. To solve the TLs model, a gradient-boosting decision tree method (GBDT) is used for feature selection. Considering the heterogeneity in products, a mixed k-mean algorithm is applied for product clustering and a genetic algorithm for parameter estimation in each cluster. The model is tested on real-world data from a Singapore company, and the experimental results show that our model is better than LR, GBDT, support vector regression (SVR) and artificial neural network (ANN) in most cases. Furthermore, two indicators are built: the average conversion rate and the marginal conversion rate, to measure products’ competitiveness and explore the optimal inventory level, respectively, which provide helpful guidance on decision-making for fashion industry managers.

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Czekala, Mariusz. "Condition Analysis and Forecasting in the Fashion Industry." International Journal of Economics, Finance and Management Sciences 7, no. 2 (2019): 74. http://dx.doi.org/10.11648/j.ijefm.20190702.14.

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Yu, Yong, Tsan-Ming Choi, Kin-Fan Au, and Chui-Yan Kwan. "A Web-Based System for Fashion Sales Forecasting." Research Journal of Textile and Apparel 12, no. 3 (August 2008): 56–64. http://dx.doi.org/10.1108/rjta-12-03-2008-b006.

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Wazarkar, Seema, and Bettahally N. Keshavamurthy. "Social image mining for fashion analysis and forecasting." Applied Soft Computing 95 (October 2020): 106517. http://dx.doi.org/10.1016/j.asoc.2020.106517.

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Noh, Mijeong, and Pamela Ulrich. "Querying fashion professionals’ forecasting practices: the Delphi method." International Journal of Fashion Design, Technology and Education 6, no. 1 (March 2013): 63–70. http://dx.doi.org/10.1080/17543266.2013.765510.

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Giri, Chandadevi, and Yan Chen. "Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry." Forecasting 4, no. 2 (June 20, 2022): 565–81. http://dx.doi.org/10.3390/forecast4020031.

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Compared to other industries, fashion apparel retail faces many challenges in predicting future demand for its products with a high degree of precision. Fashion products’ short life cycle, insufficient historical information, highly uncertain market demand, and periodic seasonal trends necessitate the use of models that can contribute to the efficient forecasting of products’ sales and demand. Many researchers have tried to address this problem using conventional forecasting models that predict future demands using historical sales information. While these models predict product demand with fair to moderate accuracy based on previously sold stock, they cannot fully be used for predicting future demands due to the transient behaviour of the fashion industry. This paper proposes an intelligent forecasting system that combines image feature attributes of clothes along with its sales data to predict future demands. The data used for this empirical study is from a European fashion retailer, and it mainly contains sales information on apparel items and their images. The proposed forecast model is built using machine learning and deep learning techniques, which extract essential features of the product images. The model predicts weekly sales of new fashion apparel by finding its best match in the clusters of products that we created using machine learning clustering based on products’ sales profiles and image similarity. The results demonstrated that the performance of our proposed forecast model on the tested or test items is promising, and this model could be effectively used to solve forecasting problems.

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Xie, Gang, Yingxue Zhao, Mao Jiang, and Ning Zhang. "A Novel Ensemble Learning Approach for Corporate Financial Distress Forecasting in Fashion and Textiles Supply Chains." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/493931.

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This paper proposes a novel ensemble learning approach based on logistic regression (LR) and artificial intelligence tool, that is, support vector machine (SVM) and back-propagation neural networks (BPNN), for corporate financial distress forecasting in fashion and textiles supply chains. Firstly, related concepts of LR, SVM, and BPNN are introduced. Then, the forecasting results by LR are introduced into the SVM and BPNN techniques which can recognize the forecasting errors in fitness by LR. Moreover, empirical analysis of Chinese listed companies in fashion and textile sector is implemented for the comparison of the methods, and some related issues are discussed. The results suggest that the proposed novel ensemble learning approach can achieve higher forecasting performance than those of individual models.

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Fumi, Andrea, Arianna Pepe, Laura Scarabotti, and Massimiliano M. Schiraldi. "Fourier Analysis for Demand Forecasting in a Fashion Company." International Journal of Engineering Business Management 5 (January 1, 2013): 30. http://dx.doi.org/10.5772/56839.

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In the fashion industry, demand forecasting is particularly complex: companies operate with a large variety of short lifecycle products, deeply influenced by seasonal sales, promotional events, weather conditions, advertising and marketing campaigns, on top of festivities and socio-economic factors. At the same time, shelf-out-of-stock phenomena must be avoided at all costs. Given the strong seasonal nature of the products that characterize the fashion sector, this paper aims to highlight how the Fourier method can represent an easy and more effective forecasting method compared to other widespread heuristics normally used. For this purpose, a comparison between the fast Fourier transform algorithm and another two techniques based on moving average and exponential smoothing was carried out on a set of 4-year historical sales data of a €60+ million turnover medium- to large-sized Italian fashion company, which operates in the women's textiles apparel and clothing sectors. The entire analysis was performed on a common spreadsheet, in order to demonstrate that accurate results exploiting advanced numerical computation techniques can be carried out without necessarily using expensive software.

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Laaziz, El Hassan. "AI based forecasting in fast fashion industry: a review." IOP Conference Series: Materials Science and Engineering 827 (June 11, 2020): 012065. http://dx.doi.org/10.1088/1757-899x/827/1/012065.

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Seifert, Matthias, Enno Siemsen, Allègre L. Hadida, and Andreas B. Eisingerich. "Effective judgmental forecasting in the context of fashion products⋆." Journal of Operations Management 36, no. 1 (February 28, 2015): 33–45. http://dx.doi.org/10.1016/j.jom.2015.02.001.

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Xia, Min, and W. K. Wong. "A seasonal discrete grey forecasting model for fashion retailing." Knowledge-Based Systems 57 (February 2014): 119–26. http://dx.doi.org/10.1016/j.knosys.2013.12.014.

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Choi, Tsan-Ming, Chi-Leung Hui, Na Liu, Sau-Fun Ng, and Yong Yu. "Fast fashion sales forecasting with limited data and time." Decision Support Systems 59 (March 2014): 84–92. http://dx.doi.org/10.1016/j.dss.2013.10.008.

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Yu, Yong, Tsan-Ming Choi, and Chi-Leung Hui. "An intelligent fast sales forecasting model for fashion products." Expert Systems with Applications 38, no. 6 (June 2011): 7373–79. http://dx.doi.org/10.1016/j.eswa.2010.12.089.

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VALIULINA, S. V. "EXPERIENCE OF FORECASTING OF FASHIONABLE TENDENCIES." Urban construction and architecture 1, no. 3 (September 15, 2011): 120–22. http://dx.doi.org/10.17673/vestnik.2011.03.25.

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Analytical gathering of various techniques in the field of forecasting of clothes, fashion is presented. Methods are based on cyclic law and represent value for economy in the field of clothes manufacture.

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Raybould, Caroline. "Trends forecasting as a tool for sustainable education." Fashion, Style & Popular Culture 00, no. 00 (February 18, 2021): 1–14. http://dx.doi.org/10.1386/fspc_00058_1.

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The fashion and textile industry is under increasing scrutiny because of its unethical and unsustainable practices. It is clear there needs to be systemic change towards a more ecological future. One way to achieve this is through education, by equipping students with strategies and skills and by nurturing sustainable mindsets. How can we create the next generation of fashion professionals who can help bring the change that is much needed? Having taught sustainability within various modules on a fashion business degree in the United Kingdom, it was observed that a significant number of students engaged at a deeper level with sustainable thinking when learning trends forecasting research. A pilot study was trialled when teaching a short course in India with a small group of interdisciplinary design students and a questionnaire was conducted after the workshop. This article presents findings and reflections of this cross-cultural experience, with suggestions for future projects and educational approaches.

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Kim, Eundeok, and Kim K. P. Johnson. "Forecasting the US fashion industry with industry professionals – part 2." Journal of Fashion Marketing and Management: An International Journal 13, no. 2 (May 8, 2009): 268–78. http://dx.doi.org/10.1108/13612020910957752.

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Kim, Eundeok, and Kim K. P. Johnson. "Forecasting the US fashion industry with industry professionals – part 1." Journal of Fashion Marketing and Management: An International Journal 13, no. 2 (May 8, 2009): 256–67. http://dx.doi.org/10.1108/13612020910957761.

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Rubio, Lihki, Alejandro J. Gutiérrez-Rodríguez, and Manuel G. Forero. "EBITDA Index Prediction Using Exponential Smoothing and ARIMA Model." Mathematics 9, no. 20 (October 9, 2021): 2538. http://dx.doi.org/10.3390/math9202538.

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Forecasting has become essential in different economic sectors for decision making in local and regional policies. Therefore, the aim of this paper is to use and compare performance of two linear models to predict future values of a measure of real profit for a group of companies in the fashion sector, as a financial strategy to determine the economic behavior of this industry. With forecasting purposes, Exponential Smoothing (ES) and autoregressive integrated moving averages (ARIMA) models were used for yearly data. ES and ARIMA models are widely used in statistical methods for time series forecasting. Accuracy metrics were used to select the model with best performance and ES parameters. For the real profit measure of the financial performance of the fashion sector in Colombia EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) was used and was calculated using multiple SQL queries.

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Ballmer, Amy, and Jennifer Tobias. "Trend forecasting: Collecting the history of the future." Art Libraries Journal 42, no. 1 (December 15, 2016): 19–25. http://dx.doi.org/10.1017/alj.2016.40.

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How do art and design libraries collect the history of the future? Trend forecasting literature presents exactly that challenge. These multifaceted print and digital publications, issued regularly and expensively by a handful of companies, are held by few libraries even as they influence everything from womenswear to computer games. We examine how libraries collect these materials and consider their role in the broader information landscape.First, we historically situate forecasting, looking to origins in colour charts, trade catalogues and international communications. Next, we look at the post-war institutionalization of trend forecasting, describing its role in the consolidation of a consumer-oriented supply chain.With the Fashion Institute of Technology as the case study, we then examine forecasting in context: how faculty incorporate it into pedagogy, how students engage with the materials and how librarians integrate critical thinking and information literacy into instruction. Practical matters such as cost, housing, long-term archiving and access are also addressed.We conclude with a forecast of forecasting, examining its move to digital formats and the challenge of meeting pedagogical needs that are at once rigorous (as accreditation demands) and creative (as schools promise), reflecting the mash-up wonder of today's fashion discourse.

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Li, Han, and Colin O’Hare. "Mortality Forecasting: How Far Back Should We Look in Time?" Risks 7, no. 1 (February 22, 2019): 22. http://dx.doi.org/10.3390/risks7010022.

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Extrapolative methods are one of the most commonly-adopted forecasting approaches in the literature on projecting future mortality rates. It can be argued that there are two types of mortality models using this approach. The first extracts patterns in age, time and cohort dimensions either in a deterministic fashion or a stochastic fashion. The second uses non-parametric smoothing techniques to model mortality and thus has no explicit constraints placed on the model. We argue that from a forecasting point of view, the main difference between the two types of models is whether they treat recent and historical information equally in the projection process. In this paper, we compare the forecasting performance of the two types of models using Great Britain male mortality data from 1950–2016. We also conduct a robustness test to see how sensitive the forecasts are to the changes in the length of historical data used to calibrate the models. The main conclusion from the study is that more recent information should be given more weight in the forecasting process as it has greater predictive power over historical information.

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Marsetiani, Marcella. "Model Optimasi Penentuan Kombinasi Produk Menggunakan Metode Linear Programming pada Perusahaan Bidang Fashion." Winners 15, no. 1 (March 31, 2014): 1. http://dx.doi.org/10.21512/tw.v15i1.630.

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The aims of this research are to determine the most optimal method for sales forecasting, to show the number of sales forecast on March 2014, and to determine the appropriate and efficient product mix should be produced by PT Astakarya Busanaprima within the fashion industry. This research used six forecasting methods and linear programming technique by maximize function with eight decision variables: jackets, dresses, blouses, kebaya, caftans, skirts, pants, and shawls also with constrained factors: available time for work, primarily material, supporting material, and demand fluctuation. The results show that the best forecasting method for the company was linear regression which predicted that 17 jackets, 247 dresses, 78 blouses, 42 kebaya, 12 caftans, 15 skirts, 17 pants, and 4 shawls would be sold on March 2014. The appropriate and efficient product mix that should be produced on March 2014 are 17 jackets, 70 dresses, 78 blouses, 42 kebaya, 12 caftans, 15 skirts, 17 pants, and 4 shawls.

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Ghosh, Reeta. "FORECASTING OF COLOURS IN CREATIVE COMPOSITION OF TEXTILE FABRIC AND FASHION." International Journal of Research -GRANTHAALAYAH 2, no. 3SE (December 31, 2014): 1–3. http://dx.doi.org/10.29121/granthaalayah.v2.i3se.2014.3520.

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Colour is the most enriched and endless form of human life. It reflects our mood and more importantly, it has more influence and effect on our lives than we realize. How can we define colour, a reality, a feeling that creates energy, enthusiasm, potential, anger, affection, passion andmany other feelings in us. If we are in pleasant feeling, colour fills us with the feel of joy and happiness and in contrary to this if we are unhappy and sad the colours are there, who fill with the feel of unhealthy, unhappiness and sadness. Now it is a matter to think upon that why we correlate our feelings with colour or how colour reflects our mood or our internal expressions in a physical manner. It is not only because of colour but of course because of its composition too. We always look for a new colour palette range or a new invention but have we ever thought that from where did colours come and why they show their impact on us and our lives. Craving for colour, forces human to create invent and generate inventions, of course a human with artistic or creative brain and heart, who not only evolve in engraving of colours but also put efforts in to it to foster.

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Ren, Shuyun, Tsan-Ming Choi, and Na Liu. "Fashion Sales Forecasting With a Panel Data-Based Particle-Filter Model." IEEE Transactions on Systems, Man, and Cybernetics: Systems 45, no. 3 (March 2015): 411–21. http://dx.doi.org/10.1109/tsmc.2014.2342194.

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Beheshti-Kashi, Samaneh, Hamid Reza Karimi, Klaus-Dieter Thoben, Michael Lütjen, and Michael Teucke. "A survey on retail sales forecasting and prediction in fashion markets." Systems Science & Control Engineering 3, no. 1 (December 17, 2014): 154–61. http://dx.doi.org/10.1080/21642583.2014.999389.

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Bonenberg, Wojciech. "The Trickle-up Fashion Effect in Forecasting New Trends in Architecture." Procedia Manufacturing 3 (2015): 1611–17. http://dx.doi.org/10.1016/j.promfg.2015.07.450.

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Sun, Zhan-Li, Tsan-Ming Choi, Kin-Fan Au, and Yong Yu. "Sales forecasting using extreme learning machine with applications in fashion retailing." Decision Support Systems 46, no. 1 (December 2008): 411–19. http://dx.doi.org/10.1016/j.dss.2008.07.009.

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Ni, Yanrong, and Feiya Fan. "A two-stage dynamic sales forecasting model for the fashion retail." Expert Systems with Applications 38, no. 3 (March 2011): 1529–36. http://dx.doi.org/10.1016/j.eswa.2010.07.065.

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Buddy, Julie. "Fashion and colour trends in the 1990s-forecasting into the future." Journal of the Society of Dyers and Colourists 108, no. 2 (October 22, 2008): 64–69. http://dx.doi.org/10.1111/j.1478-4408.1992.tb01416.x.

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An, Hyosun, Sunghoon Kim, and Yerim Choi. "Sportive Fashion Trend Reports: A Hybrid Style Analysis Based on Deep Learning Techniques." Sustainability 13, no. 17 (August 24, 2021): 9530. http://dx.doi.org/10.3390/su13179530.

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This study aimed to use quantitative methods and deep learning techniques to report sportive fashion trends. We collected sportive fashion images from fashion collections of the past decades and utilized the multi-label graph convolutional network (ML-GCN) model to detect and explore hybrid styles. Based on the literature review, we proposed a theoretical framework to investigate sportive fashion trends. The ML-GCN was designed to classify five style categories, “street,” “retro,” “sexy,” “modern,” and “sporty,” and the predictive probabilities of the five styles of fashion images were extracted. We statistically validated the hybrid style results derived from the ML-GCN model and suggested an application method of deep learning-based trend reports in the fashion industry. This study reported sportive fashion by hybrid style dependency, forecasting, and brand clustering. We visualized the predicted probability for a hybrid style to a three-dimensional scale expected to help designers and researchers in the field of fashion to achieve digital design innovation cooperating with deep learning techniques.

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Lee, Eun Jee, and Jee Hyun Lee. "An Analysis of Emerging Issues in Fashion System Using Future Forecasting Techniques." Korean Society of Fashion Design 22, no. 1 (March 30, 2022): 143–58. http://dx.doi.org/10.18652/2022.22.1.8.

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Li, Chongshou, and Andrew Lim. "A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing." European Journal of Operational Research 269, no. 3 (September 2018): 860–69. http://dx.doi.org/10.1016/j.ejor.2018.02.029.

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Smith, Gaye. "Inspiration and information: sources for the fashion designer and historian." Art Libraries Journal 14, no. 4 (1989): 11–16. http://dx.doi.org/10.1017/s0307472200006465.

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A number of sources of inspiration and information, in addition to books on costume history, are invaluable to the fashion designer and the historian of fashion. They include predictions of style and market trends, visual sources of creative inspiration, and a variety of forms of historical evidence. Sources of information on style and market trends include forecasting services, trade magazines, newspapers, advertising material, and fashion magazines. Sources from which the designer can draw inspiration include paintings and visual imagery from the theatre, cinema, and popular culture. Historical evidence includes portrait paintings, fashion plates and magazines, photographs, literary sources, pattern books, and trade catalogues. Above all, magazines and serial-type publications are crucially important, for the sake of their currency, and later from a historical perspective; access to magazines is facilitated by indexing services.

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Huang, He, He Huang, and Qiurui Liu. "Intelligent Retail Forecasting System for New Clothing Products Considering Stock-out." Fibres and Textiles in Eastern Europe 25 (February 28, 2017): 10–16. http://dx.doi.org/10.5604/01.3001.0010.1704.

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Improving the accuracy of forecasting is crucial but complex in the clothing industry, especially for new products, with the lack of historical data and a wide range of factors affecting demand. Previous studies more concentrate on sales forecasting rather than demand forecasting, and the variables affecting demand remained to be optimized. In this study, a two-stage intelligent retail forecasting system is designed for new clothing products. In the first stage, demand is estimated with original sales data considering stock-out. The adaptive neuro fuzzy inference system (ANFIS) is introduced into the second stage to forecast demand. Meanwhile a data selection process is presented due to the limited data of new products. The empirical data are from a Canadian fast-fashion company. The results reveal the relationship between demand and sales, demonstrate the necessity of integrating the demand estimation process into a forecasting system, and show that the ANFIS-based forecasting system outperforms the traditional ANN technique.

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Berliner, L. Mark, and Yongku Kim. "Bayesian Design and Analysis for Superensemble-Based Climate Forecasting." Journal of Climate 21, no. 9 (May 1, 2008): 1891–910. http://dx.doi.org/10.1175/2007jcli1619.1.

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Abstract The authors develop statistical data models to combine ensembles from multiple climate models in a fashion that accounts for uncertainty. This formulation enables treatment of model specific means, biases, and covariance matrices of the ensembles. In addition, the authors model the uncertainty in using computer model results to estimate true states of nature. Based on these models and principles of decision making in the presence of uncertainty, this paper poses the problem of superensemble experimental design in a quantitative fashion. Simple examples of the resulting optimal designs are presented. The authors also provide a Bayesian climate modeling and forecasting analysis. The climate variables of interest are Northern and Southern Hemispheric monthly averaged surface temperatures. A Bayesian hierarchical model for these quantities is constructed, including time-varying parameters that are modeled as random variables with distributions depending in part on atmospheric CO2 levels. This allows the authors to do Bayesian forecasting of temperatures under different Special Report on Emissions Scenarios (SRES). These forecasts are based on Bayesian posterior distributions of the unknowns conditional on observational data for 1882–2001 and climate system model output for 2002–97. The latter dataset is a small superensemble from the Parallel Climate Model (PCM) and the Community Climate System Model (CCSM). After summarizing the results, the paper concludes with discussion of potential generalizations of the authors’ strategies.

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Xia, Min, Yingchao Zhang, Liguo Weng, and Xiaoling Ye. "Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs." Knowledge-Based Systems 36 (December 2012): 253–59. http://dx.doi.org/10.1016/j.knosys.2012.07.002.

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Loureiro, A. L. D., V. L. Miguéis, and Lucas F. M. da Silva. "Exploring the use of deep neural networks for sales forecasting in fashion retail." Decision Support Systems 114 (October 2018): 81–93. http://dx.doi.org/10.1016/j.dss.2018.08.010.

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Journal articles on the topic 'Fashion forecasting'

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