Journal articles: 'Fashion trend forecasting'

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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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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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Satrio, Akbar Adhi, Tri Hasdianto Hasdianto, and Amelinda Alysia A.V.K. "PERAN TRADISI DALAM TREND FORECASTING." Serat Rupa Journal of Design 4, no. 1 (January 16, 2020): 40–50. http://dx.doi.org/10.28932/srjd.v4i1.1959.

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This study is preliminary research of "The Trend Forecasting Translation Analysis in The Context of Craft". Trend Forecasting is a developed method that aims to predict future trends through research and analysis based on factual data of phenomena that occurs during certain time frames. The process includes analyzing various factors including technological development, lifestyle, also shifts of paradigm that form strategic decisions towards product design. However, the role of the trend forecast being applied to today’s craft designing process needs to be further reviewed. It is related to the fact where traditional elements are attached to all craft products, emphasized to be well-preserved in showing its characteristics of culture and tradition. This study seeks to map where the position of tradition takes place among the trend forecasting process through the analysis of a.) field studies in institutions and practitioners in the field of trend forecast; b.) comparative study of the position of tradition in the process of trend forecasting based on three kinds of literature namely ”The Trend Forecaster’s Handbook”, ”How to Research Trends”, and ”Fashion Forecasting”. In the scope of methods, the results of this study show that tradition stands as a significant data in the process of constructing a trend forecast. However, the perspective is not under the context of local culture but as the behavior of people.

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Chakraborty, Samit, S. M. Azizul Hoque, and S. M. Fijul Kabir. "Predicting fashion trend using runway images: application of logistic regression in trend forecasting." International Journal of Fashion Design, Technology and Education 13, no. 3 (September 1, 2020): 376–86. http://dx.doi.org/10.1080/17543266.2020.1829096.

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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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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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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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DuBreuil, Mikayla, and Sheng Lu. "Traditional vs. big-data fashion trend forecasting: an examination using WGSN and EDITED." International Journal of Fashion Design, Technology and Education 13, no. 1 (January 2, 2020): 68–77. http://dx.doi.org/10.1080/17543266.2020.1732482.

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Yu, Yong, Chi-Leung Hui, and Tsan-Ming Choi. "An empirical study of intelligent expert systems on forecasting of fashion color trend." Expert Systems with Applications 39, no. 4 (March 2012): 4383–89. http://dx.doi.org/10.1016/j.eswa.2011.09.153.

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Bandlien, Charlotte Bik. "Post Luxury: Normcore as Node and Prism." APRIA Journal 1, no. 1 (February 1, 2020): 25–37. http://dx.doi.org/10.37198/apria.01.00.a5.

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'Normcore' was not only the most Googled fashion trend of 2014 but also the runner-up for neologism of the year by Oxford University Press. The phrase generated numerous headlines, such as "Normcore Is (or Is It?) a Fashion Trend (or Non-Trend or Anti-Trend)" in the Los Angeles Times in 2015 or "Everyone's Getting Normcore Wrong, Says Its Inventors" in Dazed in 2014, indicating a multi-faceted and intriguing phenomenon. This article employs the timing of post peak normcore to investigate a trend that surely entailed more than meets the eye. Described as "a unisex fashion trend characterized by unpretentious, normal-looking clothing" by Wikipedia, normcore was in fact not meant to be a trend at all, nor was it meant to be used to refer to a particular code of dress. Initially a spoof marketing term coined by the art collective/trend forecasting group K-Hole in 2013, normcore was originally a subversive concept, anticipating an alternative way forward, proposing anti-distinction as the radical new, analysed here as a mode beyond luxury—as 'post luxury'. Combining anthropology, consumption theory, and critical fashion theory with a practice-based insight informed by the author's background in trend analysis and brand planning as well as the art school context, this article attempts to frame and unpack normcore in order to speculate about the future of luxury.

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Koren, Michal, Elad Harison, and Matan Shnaiderman. "IDENTIFYING FUTURE VARIANTS OF FASHION DEMAND: A TREND FORECASTING MODEL FOR MINIMIZING SUPPLY CHAIN COSTS." Global Fashion Management Conference 3, no. 1 (June 30, 2015): 296. http://dx.doi.org/10.15444/gfmc2015.03.01.01.

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Dawelbait, Gihan, Andreas Henschel, Toufic Mezher, and Wei Lee Woon. "Forecasting Renewable Energy Technologies in Desalination and Power Generation Using Taxonomies." International Journal of Social Ecology and Sustainable Development 2, no. 3 (July 2011): 79–93. http://dx.doi.org/10.4018/ijsesd.2011070105.

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Renewable Energy (RE) technologies are increasingly viewed as crucially important. Knowledge that helps to predict the likely growth of emergent technologies is essential for well-informed technology management. The vast amount of available data in publications hinders the acquisition and analysis of this knowledge. Therefore, there is a need for intelligent search techniques capable of grouping semantically similar concepts together, such that, for example, terms containing “photovoltaic” are hierarchically subsumed under solar energy-related technologies. Consequently, articles related to “Photovoltaics” should be included in the analysis. To accommodate this in an automated fashion, the authors deploy a renewable energy taxonomy for comprehensive trend discovery in publications and patents. This taxonomy is based on the hierarchical structure of Wikipedia categories and their subordinate Wikipedia terms. This paper analyzes promising trends of renewable energy sources in two case studies: power generation and desalination techniques.

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Tao, Xiong, Li Chongguang, and Bao Yukun. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China." Agricultural Economics (Zemědělská ekonomika) 63, No. 3 (January 12, 2017): 136–48. http://dx.doi.org/10.17221/268/2015-agricecon.

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Short-term forecasting of hog price, which forms the basis for the decision making, is challenging and of great interest for hog producers and market participants. This study develops improved ensemble empirical mode decomposition (EEMD)-based hybrid approach for the short-term hog price forecasting. Specifically, the EEMD is first used to decompose the original hog price series into several intrinsic-mode functions (IMF) and one residue. The fine-to-coarse reconstruction algorithm is then applied to compose the obtained IMFs and residue into the high-frequency fluctuation, the low-frequency fluctuation, and the trend terms which can highlight new features of the hog price fluctuations. Afterwards, the extreme learning machine (ELM) is employed to model the low-frequency fluctuation, while the autoregressive integrated moving average (ARIMA) and the polynomial function are used to fit the high-frequency fluctuation and trend term, respectively, in a multistep-ahead fashion. The commonly used iterated prediction strategy is adopted for the implementation of the multistep-ahead forecasting. The monthly hog price series from January 2000 to May 2015 in China is employed to evaluate the forecasting performance of the proposed approach with the selected counterparts. The numerical results indicate that the improved EEMD-based hybrid approach is a promising alternative for the short-term hog price forecasting.

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Muis, Afni Regita Cahyani, Ali Musa Harahap, and Fadhlan Nur Hakiem. "Sustainable Competitive Advantage of Indonesia’s Creative Economics: Fashion Sub-Sector." Tourism and Sustainable Development Review 1, no. 2 (August 31, 2020): 76–86. http://dx.doi.org/10.31098/tsdr.v1i2.12.

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This research has identified the government's strategies to encourage creative fashion industries in Indonesia with its cultural branding and the applicability of sustainable competitive advantage as a concept to maintain competitiveness. This research employed a qualitative research method based on primary data, obtained from in-depth interviews at the Creative Economy Agency, the Indonesian Ministry of Trade and, the Indonesian Ministry of Industry, and the Central Statistics Agency. In analyzing the data, this research categorizes the reports and journals of the government's endeavor result. It then reduced the data by creating a discussion scheme and writing the core of each discussion component. Data is triangulated to compare the results of interviews with research objects and documents. The research found that the following are crucial strategies to empower sustainable competitive advantage of the creative economy in Indonesia's fashion subsector: Developing Priority Industry Clusters, Research and Development, Indonesia Trend Forecasting, Innovative and Creativity through Nusantara Collaboration, Modest Fashion, Intellectual Property Rights, and Economic Partnership Agreement.

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Kusimova, Tamara. "Managing Uncertainty: How Trend-forecasting Agencies Conquer the Global Fashion Industry Book Review: Lantz J. (2016) The Trendmakers: Behind the Scenes of the Global Fashion Industry, London, UK; New York: Bloomsbury Academic." Journal of Economic Sociology 18, no. 3 (2017): 130–39. http://dx.doi.org/10.17323/1726-3247-2017-3-130-139.

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Herdiyani, Nindhita Gita Puspita, Zumrotu Zakiyah, and Salsabiil Khoirunnisa. "PENGGUNAAN TEKNIK KATAZOME DAN PEWARNA KUNYIT PADA INSPIRASI MOTIF REMPAH BUSANA READY TO WEAR." Texere 19, no. 1 (June 28, 2021): 1–15. http://dx.doi.org/10.53298/texere.v19i1.01.

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Pembuatan desain motif di atas permukaan kain dilakukan dengan berbagai cara, salah satunya adalah dengan metode perintangan. Teknik perintang warna yang berkembang di negara Jepang adalah teknik katazome. Katazome adalah teknik pembuatan motif reka latar pada kain menggunakan celup rintang pasta tepung beras. Pembuatan motif dilakukan dengan cara mengoleskan pasta tepung beras di atas cetakan berupa stensil kertas (katagami). Teknik katazome memberikan alternatif bagi pelaku dan pengguna fashion dalam pembuatan desain motif dan produk pakaian jadi. Metodologi yang digunakan adalah studi pustaka dan percobaan dalam pembuatan desain motif dan produk pakaian jadi. Teknik katazome sesuai jika diterapkan untuk membuat produk yang ramah lingkungan dan dekat dengan isu sustainable fashion saat ini. Bahan utama teknik ini terbuat dari bahan yang organik dan tidak banyak menggunakan bahan kimia. Hasil produk dari penelitian ini berupa busana ready to wear yang terinspirasi dari motif rempah. Tema yang diusung yaitu pada Trend Forecasting Singularity 2019/2020 tema Svarga dengan sub tema Upskill Craft. Penerapan teknik katazome pada kain berbahan kapas (strada terracotta) terdiri dari mordanting kain, pembuatan motif pada katagami, pembuatan pasta tepung beras dan pencelupan menggunakan pewarna kunyit. Takaran resep dalam pembuatan larutan zat warna yang berasal dari kunyit, jumlah celupan dan durasi perendaman kain dalam larutan fiksasi tunjung berpengaruh terhadap ketahanan luntur warna kain yang dilakukan penerapan teknik katazome.

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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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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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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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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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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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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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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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Bakhashwain, Norah, and Alaa Sagheer. "Online Tuning of Hyperparameters in Deep LSTM for Time Series Applications." International Journal of Intelligent Engineering and Systems 14, no. 1 (February 28, 2021): 212–20. http://dx.doi.org/10.22266/ijies2021.0228.21.

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Deep learning is one of the most remarkable artificial intelligence trends. It stands behind numerous recent achievements in several domains, such as speech processing, and computer vision, to mention a few. Accordingly, these achievements have sparked great attention to employing deep learning in time series modelling and forecasting. It is known that the deep learning algorithms built on neural networks contain multiple hidden layers, which make the computation of deep neural network challenging and, sometimes, complex. The reason for this complexity is that obtaining an outstanding and consistent result from such deep architecture requires optimizing many parameters known as hyperparameters. Doubtless, hyperparameter tuning plays a critical role in improving the performance of deep learning. This paper proposes an online tuning approach for the hyperparameters of deep long short-term memory (DLSTM) model in a dynamic fashion. The proposed approach adapts to learn any time series based application, particularly the applications that contain streams of data. The experimental results show that the dynamic tuning of the DLSTM hyperparameters performs better than the original static tuning fashion.

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Parameshwarappa Rajendra Patel, Gowrav M P, Borra Vamsi, and Hemanth kumar S. "A Review on Modern Drug Packaging in Pharmaceutical Industries." International Journal of Research in Pharmaceutical Sciences 11, no. 2 (April 3, 2020): 1486–92. http://dx.doi.org/10.26452/ijrps.v11i2.2022.

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Pharmaceutical packing is one of the markets throughout the world that is progressing at continuous pace. It’s predictable that market can grow to price $78.79 Billion by 2018. Packaging may be a key available for sale, protection and accomplishment. Like different grocery, prescribed drugs packaging got to be in such a fashion that it'll give quick packaging, safety, identification, goods superiority, patient safety, display and desires of security. Packaging is an art of science where several considerations are involved from the basic development of the design and the technology implemented to pack the product without any instability and providing protection, presentation and observance of manufactured goods during transportation, storage until it reaches the Consumed. Packaging technician design containers which would maintain the physiochemical, and biological stability about the drug and package would be able to withstand the pressures that would be inflicted the entire supply and transport process. Advancement in analysis of prescribed drugs development had perpetually being obsessed with the packaging skill. Maintaining truthfulness of prescribed drugs throughout storing, cargo and transport is guaranteed by quality of packing available. This current evaluation provides an in depth study of the pharmaceutical packaging trends and forecasting the packing outcomes in future.

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Skalka, Ekaterina Valer'evna. "How to predict color?" Человек и культура, no. 6 (June 2020): 116–23. http://dx.doi.org/10.25136/2409-8744.2020.6.33036.

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The subject of this research is the forecast or prediction of color. The Russian science does not feature work dedicated to such aspect; however, foreign sources approached prediction of color from various perspectives, most often viewing the color trends as an inseparable part of fashion. The goal of this work consists in determination of the place and time of conception of the color forecasts, historical analysis of evolution of this phenomenon, and assumptions on further development of this direction. Territorially, the epicenters of development of color forecasts are determined in Europe (France and England), in America (the United States), in Asia (Japan). The author describes the peculiarities of color predictions in each country – at certain stages one or another county was ahead or behind; whit the advent of the Internet, everything moves to online format and becomes more dynamic. The article follows the forecast of color since its emergence, highlighting all stages of its development and establishment. The chronological framework of its development stages stretches from the early XVIII century until modernity. From the earliest to more recent, these stages include the color cards, mediators and agents (between factories, textile workers and customers, and stores), trade fairs and expositions, online services and websites. It is determined that with the course of time, the methods and instruments for predicting color were being accumulated and used together; the new colors were added, while the already existing did not lose their relevance. Despite the development of technologies, a final word in forecasting color trends belongs to a human, based on experience and intuition.

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Yuan, Xiaojun, Dake Chen, Cuihua Li, Lei Wang, and Wanqiu Wang. "Arctic Sea Ice Seasonal Prediction by a Linear Markov Model." Journal of Climate 29, no. 22 (October 26, 2016): 8151–73. http://dx.doi.org/10.1175/jcli-d-15-0858.1.

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Abstract A linear Markov model has been developed to predict sea ice concentration (SIC) in the pan-Arctic region at intraseasonal to seasonal time scales, which represents an original effort to use a reduced-dimension statistical model in forecasting Arctic sea ice year-round. The model was built to capture covariabilities in the atmosphere–ocean–sea ice system defined by SIC, sea surface temperature, and surface air temperature. Multivariate empirical orthogonal functions of these variables served as building blocks of the model. A series of model experiments were carried out to determine the model’s dimension. The predictive skill of the model was evaluated by anomaly correlation and root-mean-square errors in a cross-validated fashion . On average, the model is superior to the predictions by anomaly persistence, damped anomaly persistence, and climatology. The model shows good skill in predicting SIC anomalies within the Arctic basin during summer and fall. Long-term trends partially contribute to the model skill. However, the model still beats the anomaly persistence for all targeted seasons after linear trends are removed. In winter and spring, the predictability is found only in the seasonal ice zone. The model has higher anomaly correlation in the Atlantic sector than in the Pacific sector. The model predicts well the interannual variability of sea ice extent (SIE) but underestimates its accelerated long-term decline, resulting in a systematic model bias. This model bias can be reduced by the constant or linear regression bias corrections, leading to an improved correlation skill of 0.92 by the regression bias correction for the 2-month-lead September SIE prediction.

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Gesheva, Elena G. "Russia’s electoral landscape: yesterday, today, tomorrow." VESTNIK INSTITUTA SOTZIOLOGII 29, no. 2 (2019): 125–37. http://dx.doi.org/10.19181/vis.2019.29.2.580.

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In the collective monograph “Election in the context of Crimea: the 2016-2018 election cycle and prospects for a political transition”, edited by V. Fyodorov, experts from the Russia Public Opinion Research Center analyze the evolution of Russian people’s political behavior during the years 2016-2018, while revealing the prospects and risks for subsequent election cycles. Major sociological evaluations of the latest electoral campaigns served as a basis for analyzing the electoral landscape. The authors note that elections in Russia are held under a political system with limited competition, which doesn’t create any possibility for an array of alternative choices. Sociological studies show that all of the latest election cycles in Russia were conducted in the typical spirit of Weber’s plebiscitary democracy, while the main source of public trust in society is the political leader’s personality, legitimized in a paternalistic fashion. Russian people do not consider elections within the logic of rational behavior and usefulness, or personal benefit and potential gains for the country as a whole. In public consciousness overcoming economic issues is not linked to developing democratic institutions. Most people distrust the opposition, made apparent by the failed election boycott proposed by the non-system opposition, by meager signal voting etc. This collective monograph highlights the basic foundations for a “post-Crimea consensus” – rallying around a strong leader figure, intensifying patriotic attitudes within the context of returning the Crimea and in the face of western sanctions. While studying mass consciousness, the authors highlight an “intermediate” state of the value environment, which is characterized by ideological divides and separations, the main of which divides the conservative majority and the liberal minority. Such ambiguity in the realm of values provides equal grounds for stating that we are dealing with both a “post-Crimea consensus” and a “post-Crimea divide”. The “post-Crimea consensus” served not only as a means of consolidation, but also as a means of isolation and exclusion. The monograph also considers the emotional component’s effect on electoral choice. The book pays careful attention to issues with political forecasting, as well as techniques and methods used in political forecasting, which allows for highlighting the subsequent course and trends in electoral processes.

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Baneres, David, Ana Elena Guerrero-Roldán, M. Elena Rodríguez-González, and Abdulkadir Karadeniz. "A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System." Applied Sciences 11, no. 13 (June 22, 2021): 5781. http://dx.doi.org/10.3390/app11135781.

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Learning analytics is quickly evolving. Old fashioned dashboards with descriptive information and trends about what happened in the past are slightly substituted by new dashboards with forecasting information and predicting relevant outcomes about learning. Artificial intelligence is aiding this revolution. The accessibility to computational resources has increased, and specific tools and packages for integrating artificial intelligence techniques leverage such new analytical tools. However, it is crucial to develop trustworthy systems, especially in education where skepticism about their application is due to the risk of teachers’ replacement. However, artificial intelligence systems should be seen as companions to empower teachers during the teaching and learning process. During the past years, the Universitat Oberta de Catalunya has advanced developing a data mart where all data about learners and campus utilization are stored for research purposes. The extensive collection of these educational data has been used to build a trustworthy early warning system whose infrastructure is introduced in this paper. The infrastructure supports such a trustworthy system built with artificial intelligence procedures to detect at-risk learners early on in order to help them to pass the course. To assess the system’s trustworthiness, we carried out an evaluation on the basis of the seven requirements of the European Assessment List for trustworthy artificial intelligence (ALTAI) guidelines that recognize an artificial intelligence system as a trustworthy one. Results show that it is feasible to build a trustworthy system wherein all seven ALTAI requirements are considered at once from the very beginning during the design phase.

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Zhao, Li, Muzhen Li, and Peng Sun. "Neo-Fashion: A Data-Driven Fashion Trend Forecasting System Using Catwalk Analysis." Clothing and Textiles Research Journal, March 29, 2021, 0887302X2110042. http://dx.doi.org/10.1177/0887302x211004299.

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Trend forecasting is a challenging and important aspect of the fashion industry. The authors design a novel fashion trend analysis system called “Neo-Fashion,” which provides recommendations to fashion researchers and practitioners about potential fashion trends using computer vision and machine learning. Neo-Fashion includes three modules, a data collection and labeling module, an instance segmentation module and a trend analysis module. Diffusion of innovation theory is used as the main theoretical framework to understand fashion trends. 32,702 catwalk images from 2019 fashion week were collected, and 769 images were labeled as training data. Neo-fashion is able to identify and segment fashion items in the given images, and indicate the fashion trends in colors, styles, clothing combinations, and other fashion attributes. To optimize the system, more data sources can be included to not only reflect trends in even more categories but also aid in understanding the trickle-up or trickle-across process in fashion.

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Shi, Mengyun, Cali Chussid, Pinyi Yang, Menglin Jia, Van Dyk Lewis, and Wei Cao. "The exploration of artificial intelligence application in fashion trend forecasting." Textile Research Journal, March 31, 2021, 004051752110062. http://dx.doi.org/10.1177/00405175211006212.

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Fashion trends today are changing much faster than ever before. Timely and reliable trend forecasting is, therefore, critical in the fashion industry. Traditional fashion forecasting requires professionals to abstract image-based information across design collections and time intervals from around the world, which is extremely time-consuming and labor intensive. Considering the financial cost associated with manual labeling and the accuracy of classifications based upon human subjective judgment, this explorative study proposes a data-driven quantitative abstracting approach using an artificial intelligence (A.I.) algorithm. Firstly, an A.I. model was trained to be familiar with fashion images from a large-scale dataset under different scenarios such as online stores and street snapshots; secondly, the model could detect garments and classify clothing attributes such as fabric textures, garment style, and design details from runway photos and videos; thirdly, the model could summarize fashion trends from the attributes it developed. The adoption of an A.I. algorithm proved to be an objective and systematic computerized method of interpreting fashion dynamics in a more efficient, accurate, sustainable, and cost-effective way.

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Garcia, Clarice Carvalho. "Fashion forecasting: an overview from material culture to industry." Journal of Fashion Marketing and Management: An International Journal ahead-of-print, ahead-of-print (July 27, 2021). http://dx.doi.org/10.1108/jfmm-11-2020-0241.

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PurposeAlthough writings in the fashion forecasting field often mention the connections between industry and culture, it still requires further clarifications in a context of uncertainty, fast pace changes and a high volume of information. This paper aims to explore fashion as a material culture to discuss forecasting roles in different stages of dialogue between culture and industry.Design/methodology/approachThis paper explores the cultural aspects of fashion to discuss multiple roles of forecasting and its implications in the fashion system from a multidimensional perspective that interlaces culture and industry in contemporary contexts through a literature review in fashion forecasting and material culture. Recent nonacademic articles were also reviewed in order to highlight fresh perspectives in the field.FindingsThe literature review demonstrates that there are two main lines of reasoning in trend forecasting. First, trend forecasting as a cultural and predictive practice focused on understanding emerging shifts in the culture and translating them to the industry. The second approach considers trend forecasting as a strategic and curatorial practice that not merely predicts consumer's behaviors and preferences but intentionally acts as a filter of all the available possibilities curating and narrowing them down to organize the market around assertive information reducing financial losses risk. This article proposes an integration between the two perspectives – from culture to industry – in a contemporary context where consumers' tastes and preferences have become increasingly diverse, and early diffusion theories can no longer explain fashion spread.Research limitations/implicationsFurther investigations of contemporary and potential future trend forecasting roles and aspects could benefit from in-depth interviews and focus groups with industry experts, consumers and academics.Practical implicationsThe paper intends to approximate theoretical reflections of fashion as a material culture to the current industry context.Originality/valueIt contributes to the studies of fashion forecasting, providing an overview of its development, roles and objectives, both from the industrial and material culture perspectives, which culminates in a framework that summarizes its intricate mechanisms.

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An, Hyosun, and Minjung Park. "Approaching fashion design trend applications using text mining and semantic network analysis." Fashion and Textiles 7, no. 1 (November 5, 2020). http://dx.doi.org/10.1186/s40691-020-00221-w.

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Abstract This study aims to identify fashion trends with design features and provide a consumer-driven fashion design application in digital dynamics, by using text mining and semantic network analysis. We examined the current role and approach of fashion forecasting and developed a trend analysis process using consumer text data. This study focuses on analyzing blog posts regarding fashion collections. Specifically, we chose the jacket as our fashion item to produce practical results for our trend report, as it is an item used in multiple seasons and can be representative of fashion as a whole. We collected 29,436 blog posts from the past decade that included the keywords “jacket” and “fashion collection.” After the data collection, we established a list of fashion trend words for each design feature by classifying styles (e.g., retro), colors (e.g., black), fabrics (e.g., leather), and patterns (e.g., checkered). A time-series cluster analysis was used to categorize fashion trends into four clusters—increasing, decreasing, evergreen, and seasonal trends—and a semantic network analysis visualized the latest season’s dominant trends along with their corresponding design features. We concluded that these results are useful as they can reduce the time-consuming process of fashion trend analysis and offer consumer-driven fashion design guidelines.

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Harison, Elad. "Identifying Future Demand in Fashion Goods: Towards Data Driven Trend Forecasting." Journal of Textile Science & Fashion Technology 3, no. 3 (August 2, 2019). http://dx.doi.org/10.33552/jtsft.2019.03.000565.

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Ding, Yujuan, Yunshan Ma, Lizi Liao, Waikeung Wong, and Tat-Seng Chua. "Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media." IEEE Transactions on Multimedia, 2021, 1. http://dx.doi.org/10.1109/tmm.2021.3078907.

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Han, Ahyoung, Jihoon Kim, and Jaehong Ahn. "Color Trend Analysis using Machine Learning with Fashion Collection Images." Clothing and Textiles Research Journal, March 3, 2021, 0887302X2199594. http://dx.doi.org/10.1177/0887302x21995948.

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Fashion color trends are an essential marketing element that directly affect brand sales. Organizations such as Pantone have global authority over professional color standards by annually forecasting color palettes. However, the question remains whether fashion designers apply these colors in fashion shows that guide seasonal fashion trends. This study analyzed image data from fashion collections through machine learning to obtain measurable results by web-scraping catwalk images, separating body and clothing elements via machine learning, defining a selection of color chips using k-means algorithms, and analyzing the similarity between the Pantone color palette (16 colors) and the analysis color chips. The gap between the Pantone trends and the colors used in fashion collections were quantitatively analyzed and found to be significant. This study indicates the potential of machine learning within the fashion industry to guide production and suggests further research expand on other design variables.

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Martina, Tina, Wine Regyandhea Putri, Irfandhani Fauzi, and Haifa Bilqis. "APLIKASI KAIN TENUN SUMBA PAHIKUNG PADA BUSANA READY TO WEAR." Texere 16, no. 2 (August 28, 2020). http://dx.doi.org/10.53298/texere.v16i2.10.

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Tenun merupakan salah satu warisan budaya tinggi kebanggaan dan jati diri bangsa Indonesia. Oleh sebab itu, tenun baik dari segi teknik produksi, desain dan produk yang dihasilkan harus dijaga dan dilestarikan keberadaannya, serta dimasyarakatkan penggunaannya. Salah satu daerah yang tetap melestarikan kain tenun yaitu Sumba Pahikung. Untuk lebih mengenalkan pada khalayak dan melestarikan kain tenun Sumba Pahikung, maka disusun rencana penelitian ini yang bertujuan untuk mengangkat dan mengembangkan muatan lokal Sumba berupa ragam hias yang diaplikasikan pada busana ready to wear. Desain perancangan busana ready to wear yang memiliki inspirasi warna dan garis desain dengan tema Vigilant dari Trend Forecasting Greyzone Fashion 2017-2018 Bekraf. Produk pakaian jadi dengan aplikasi kain tenun Sumba Pahikung akan dinilai secara ekonomi serta dengan metode kuantitatif berupa kuisioner yang disebar ke masyarakat akan dianalisa kelayakan harga dan kualitas yang dihasilkan. Dua desain busana ready to wear yang dibuat dapat diterima masyarakat secara kualitas disain etnik, akan tetapi saat dinilai secara harga masih ada sedikit perbedaan dengan perhitungan biaya produksinya.

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Elnashar, Elsayed Ahmed. "Utilization of Forecasting Global Trends in Fashion and its Applications." Latest Trends in Textile and Fashion Designing 1, no. 3 (January 30, 2018). http://dx.doi.org/10.32474/lttfd.2018.01.000113.

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Journal articles on the topic 'Fashion trend forecasting'

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