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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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

Торговля вручную на фондовой бирже постепенно уходит в прошлое. На смену идёт прогресс информационных технологий. Существуют успешные трейдеры, добившиеся высоких результатов при совершении спекулятивных операций на рынке ценных бумаг. Но на их торговые решения влияет множество психологических факторов. Альтернативой психологическим факторам человека на бирже могут быть только трейд-бот, или, торговые роботы. Это особенно актуально для трейдеров, у которых недостаточно времени, чтобы вести торговлю самостоятельно. Торговля с помощью роботов позволяет зарабатывать, занимаясь своими делами. Торговый робот – это программный комплекс, в который заложен алгоритм совершения операций на рынке ценных бумаг. Роботы могут исключить любой вид риска. У компьютерной программы отсутствуют эмоции, присущие человеку, а значит, принятое программой решение является верным в рамках заданного алгоритма, созданного человеком. Гипотеза исследования - разработка методики прогнозирования краткосрочных и среднесрочных трендов с использованием трейд-бота может позволить облегчить торговлю для как начинающих, так и профессиональных трейдеров, также позволит приумножить денежный капитал. / Manual trade on the stock exchange gradually consigns to the past. For change there is a progress of information technologies. There are successful traders who achieved good results when making speculative operations on securities market. But their trade decisions are influenced by a set of psychological factors. Trade bot or trade robots can only be an alternative to psychological factors of the person at the exchange. It is especially relevant for traders who have not enough time to do business independently. Trade by means of robots allows to earn, going about own business. Trade robot is a program complex in which algorithm of making of operations on securities market is put. Robots can exclude any kind of risk. Computer program has no emotions inherent to person, so, decision made by the program is correct within the given algorithm created by the person. Research hypothesis - development of a technique of prediction of short-term and medium-term trends with use of a trade bot is able to afford to facilitate trade for as beginning, and professional traders, monetary capital will also allow to increase.

O estudo de séries temporais para a geração de conhecimento é uma área que vem crescendo em importância e complexidade ao longo da última década, à medida que a quantidade de dados armazenados cresce exponencialmente. Considerando este cenário, novas técnicas de mineração de dados têm sido constantemente desenvolvidas para lidar com esta situação. Neste trabalho é proposto o estudo de séries temporais baseado em suas características topológicas, observadas em uma rede complexa gerada com os dados da série temporal. Especificamente, o objetivo do modelo proposto é criar um algoritmo de detecção de tendências para séries temporais estocásticas baseado em detecção de comunidades e caminhadas nesta mesma rede. O modelo proposto apresenta algumas vantagens em relação à métodos tradicionais, como o número adaptativo de classes, com força mensurável, e uma melhor absorção de ruídos. Resultados experimentais em bases artificiais e reais mostram que o método proposto é capaz de classificar as séries temporais em padrões locais e globais, melhorando a previsibilidade das séries ao se utilizar métodos de aprendizado de máquina para a previsão das classes / Extracting knowledge from time series analysis has been growing in importance and complexity over the last decade as the amount of stored data has increased exponentially. Considering this scenario, new data mining techniques have continuously developed to deal with such a situation. In this work, we propose to study time series based on its topological characteristics, observed on a complex network generated from the time series data. Specifically, the aim of the proposed model is to create a trend detection algorithm for stochastic time series based on community detection and network metrics. The proposed model presents some advantages over traditional time series analysis, such as adaptive number of classes with measurable strength and better noise absorption. Experimental results on artificial and real datasets shows that the proposed method is able to classify the time series into local and global patterns, improving the predictability of the series when using machine-learning methods

O estudo de séries temporais para a geração de conhecimento é uma área que vem crescendo em importância e complexidade ao longo da última década, à medida que a quantidade de dados armazenados cresce exponencialmente. Considerando este cenário, novas técnicas de mineração de dados têm sido constantemente desenvolvidas para lidar com esta situação. Neste trabalho é proposto o estudo de séries temporais baseado em suas características topológicas, observadas em uma rede complexa gerada com os dados da série temporal. Especificamente, o objetivo do modelo proposto é criar um algoritmo de detecção de tendências para séries temporais estocásticas baseado em detecção de comunidades e caminhadas nesta mesma rede. O modelo proposto apresenta algumas vantagens em relação à métodos tradicionais, como o número adaptativo de classes, com força mensurável, e uma melhor absorção de ruídos. Resultados experimentais em bases artificiais e reais mostram que o método proposto é capaz de classificar as séries temporais em padrões locais e globais, melhorando a previsibilidade das séries ao se utilizar métodos de aprendizado de máquina para a previsão das classes / Extracting knowledge from time series analysis has been growing in importance and complexity over the last decade as the amount of stored data has increased exponentially. Considering this scenario, new data mining techniques have continuously developed to deal with such a situation. In this work, we propose to study time series based on its topological characteristics, observed on a complex network generated from the time series data. Specifically, the aim of the proposed model is to create a trend detection algorithm for stochastic time series based on community detection and network metrics. The proposed model presents some advantages over traditional time series analysis, such as adaptive number of classes with measurable strength and better noise absorption. Experimental results on artificial and real datasets shows that the proposed method is able to classify the time series into local and global patterns, improving the predictability of the series when using machine-learning methods

Sedan millenniumskiftet förändrades modevärlden radikalt av fler säsonger, ökad mångfald av trender samtidigt som trendernas livslängds blev allt kortare. Hos inköparna på de stora klädbolagen ställs det idag ett allt större krav att veta vad som kommer att sälja i framtiden. Idag måste besluten tas snabbt så att kläderna hinner producera och levereras i tid innan modet hinner ändra sig. Som ett hjälpmedel för inköpare och designers om i vilken riktning modet kommer att gå finns trender som fungerar som ett avgränsat modeuttryck för att förmedla och tolka en viss stil och därmed ge en tydlig bild om vad som bör produceras och efterfrågas. Men eftersom internet har medfört en ökad acceleration av trender kan det upplevas svårt att veta vad som kommer sälja i framtiden och det är här ser vi trendbyråernas roll. Trendbyråer har en koordinerande roll på marknaden där de ger företag vägledning om förändringar i modet. Genom trendanalyser får företag en överblick hur riktningen i modet kommer att gå, förstå vilka globala influenser som påverkar modet och en reflektion kring hur allt återspeglas till modet. Vad gäller tidigare forskning av begreppen trender och trendbyråer vid beslutsfattande, har ingen omfattande undersökning tidigare gjorts vilket har medfört att vår uppsats känns värdefull för en mer förståelse kring ämnet. Avsikten med vår uppsats är att undersöka och analysera hur trender och trendanalyser har för betydelse vid beslutsfattande inom inköpsprocessens tidiga stadium hos svenska modeföretag. Studien har efter insamling av teori inom områdena beslut, trender och trendanalyser genomförts med en kvalitativ grund där insamling av det empiriska materialet har utgått från semistrukturerade intervjuer. Intervjuer har främst genomförts med respondenter som innehar en roll inom inköp på svenska modeföretag som ingår i beteckningen SME (Small medium Enterprises). Dessa företag är Ellos, Lindex, Gina Tricot och MQ. För att öka förståelsen kring trender och trendanalyser har intervjuer även genomförts med en trendanalytiker från Svenska Moderådet och en författare bakom boken Trendmakarna. Insamlad data har därefter analyserats och ställts mot teorier angående beslut, trender och trendanalyser i syfte att kunna uttala oss om hur inköpare ser på trender och trendbyråers betydelse vid beslutsfattande av inköp. De resultat som empirin bestått av ger indikationer på att trender har en betydande roll men att det både kan öka och minska på osäkerheter samt risker. Inköparna anser att det finns många risker med trendbaserade produkter men att trendanalyser vid beslutsfattande kan användas för att förstå riktningen modet går i för att därefter kunna anpassa inköpen genom volym, budget, material och leverantörer. Därmed kan de vara ett hjälpmedel för att minska på både ett besluts risk och osäkerhet, vilket bekräftar de teorier som tagits upp. Dock kan vi uttala oss om att beslutsfattande inom inköp inte bara baseras på trendanalyser utan andra faktorer och variabler spelar också in.