Relevant bibliographies by topics / Trends forecasting

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

Academic literature on the topic 'Trends forecasting'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Trends forecasting"

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Rozhkov, A. "Forecasting Russian Stock Market Trends." Voprosy Ekonomiki, no. 8 (August 20, 2003): 81–94. http://dx.doi.org/10.32609/0042-8736-2003-8-81-94.

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The article is devoted to investigating methods for forecasting long-term Russian stock market trends. The purpose of research is creation of the forecasting model capable of forming a reverse trend signal in the stock market. The index of trend forecasting constructed in the article includes different economic indicators and thus has high forecasting ability.
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Hook, Kristina. "Forecasting Policy Trends." Anthropology News 59, no. 4 (2018): e96-e99. http://dx.doi.org/10.1111/an.907.

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Luski, I., and J. Weinblatt. "Forecasting Productivity Trends." IFAC Proceedings Volumes 19, no. 10 (1986): 113–18. http://dx.doi.org/10.1016/s1474-6670(17)59651-2.

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Cristofolini, I., G. Mancuso, and G. Wolf. "Forecasting Defective Quality Trends." Measurement and Control 34, no. 4 (2001): 103–4. http://dx.doi.org/10.1177/002029400103400403.

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Skvarciany, Viktorija, and Justina Simanavičiūtė. "Bank Profitability Analysis and Forecasting: Lithuania Case." Trends Economics and Management 12, no. 32 (2018): 101. http://dx.doi.org/10.13164/trends.2018.32.101.

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Yedage, Satish. "Fashion Trends Prediction Tool." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42029.

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Fashion trend prediction plays a vital role in helping brands stay competitive by responding to rapidly changing consumer preferences. Traditional forecasting methods are increasingly being replaced by machine learning (ML) models that utilize vast datasets from past sales, customer preferences, and social media activity. These ML-based models provide a more accurate and dynamic approach to identifying emerging trends. This project focuses on developing an ML-driven model that predicts fashion trends by analyzing historical sales data and customer preferences. Techniques like clustering are used to segment consumers based on purchasing behavior, while neural networks and social media analysis help identify emerging styles and trends, as demonstrated in studies by Iqbal and Khan (2021) and Park and Kim (2022). Integrating multiple data sources, including customer feedback and external factors like weather patterns, enhances the prediction’s accuracy and adaptability. The power of ML lies in its ability to process real-time data, allowing brands to adjust quickly to market shifts. As consumer preferences evolve, these models continuously refine predictions, improving demand forecasting and inventory management. By aligning fashion offerings with emerging trends, this approach ensures a more personalized experience for customers and enhances brand competitiveness. Ultimately, integrating ML with customer-centric data allows for more precise forecasting, ensuring that fashion brands can predict trends and make data-driven decisions with confidence. Keywords: Fashion trend forecasting, hybrid model, customer preferences, personalization, data analytics
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Klosowski, Agnieszka. "CONDITION ANALYSIS AND FORECASTING IN THE FASHION INDUSTRY: A COMPREHENSIVE STUDY." International Journal of Economics Finance & Management Science 08, no. 07 (2023): 16–19. http://dx.doi.org/10.55640/ijefms-9132.

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Condition analysis and forecasting play a crucial role in the fashion industry, enabling businesses to make informed decisions regarding product development, inventory management, and market positioning. This comprehensive study aims to analyze the current condition of the fashion industry and develop forecasting models to anticipate future trends. Through data collection, market research, and statistical analysis, key factors influencing fashion trends, consumer preferences, and market dynamics are identified. Various forecasting methods, including trend analysis, predictive modeling, and data mining techniques, are employed to generate accurate forecasts for product demand, market growth, and emerging fashion trends. The findings provide valuable insights for fashion businesses to stay competitive, optimize their operations, and meet the evolving demands of the market.
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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 (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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Mills, Terence C. "Forecasting obesity trends in England." Journal of the Royal Statistical Society: Series A (Statistics in Society) 172, no. 1 (2009): 107–17. http://dx.doi.org/10.1111/j.1467-985x.2008.00550.x.

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Gardner, Everette S., and Ed Mckenzie. "Forecasting Trends in Time Series." Management Science 31, no. 10 (1985): 1237–46. http://dx.doi.org/10.1287/mnsc.31.10.1237.

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Dissertations / Theses on the topic "Trends forecasting"

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Wang, Fei. "Trend Transference: How to Transfer Trends to Design Concepts." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1282320567.

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CAMPOS, MARIA APARECIDA DE MORAES SIQUEIRA. "TRENDS FORECASTING: STRATEGIC ORIENTATION IN JEWELLERY DESIGN." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=10365@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>O mundo dos produtos, como as jóias, perfumes, alimentos ou automóveis, não escapa da necessidade de uma contínua renovação e antecipação. A pesquisa de tendências, enquanto observadora da sociedade e seu contexto econômico-político-cultural, torna-se essencial para o design no desenvolvimento de produtos. Esta dissertação propõe a construção de uma fundamentação teórica para a conceituação das tendências de moda e design, a atuação do pesquisador, a prática das pesquisas de tendências e sua relevância na orientação estratégica do design. Apresenta, também, a importância do design dentro do gerenciamento estratégico das empresas e algumas iniciativas que contribuíram para a consolidação do design de jóias brasileiro e suas mudanças de paradigmas, como o impacto da industrialização, a valorização do conteúdo simbólico na concepção e consumo das jóias, a aproximação das jóias com a moda e o fortalecimento da joalheria de arte. Por último, apresenta a contribuição do Caderno de Tendências de Jóias para a atualização e sucesso do design de jóias brasileiro no Mundo.<br>The world of products, as jewels, perfums, foods or automobiles, doesn´t escape of a continuous renovation and anticipation necessity. Trends forecast while observing of the society and its economic-political- cultural context becomes essential for design in the products development. This dissertation considers the construction of a theoretical fundamentation for the fashion and design trends conceptualization, the forecaster´s performance, the trends forecasts practice and its relevance in design strategical orientation. It also presents the importance of design inside the strategical management of the companies and some initiatives that had contributed to the consolidation of the brazilian jewellery design and its paradigms changes, as the impact of industrialization, the symbolic content valuation in the jewels conception and consumption, the approach of jewels and fashion and the strengthening of art´s jewellery. Finally, presents the brazilian Jewellery Trendsbook contribution for the update and success of brazilian jewellery design in the World.
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Akintoye, Sunday Akintola. "Construction tender price index : modelling and forecasting trends." Thesis, University of Salford, 1991. http://usir.salford.ac.uk/14768/.

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The thesis considers the construction tender price index, an important area of construction economics, and models are developed to fit the trends in this index. Between 1980 and 1987, the UK Building Cost Index produced by the Building Cost Information Service increased at an annual rate of 6.3% compared with Tender Price Index 3.3% and Retail Price Index at 6.7% per annum. This significant disparity between Tender Price and Building Cost Index is unexpected in view of the attributed importance of input prices in the tender price formation. This suggests that other factors apart from input prices may be responsible for the trends in building prices generally. The thesis reviews the pricing strategies of construction contractors leading to the conclusion that macroeconomic factors are equally important. A univariate analysis of 24 potential indicators of tender price trends identified some variables of importance. An analysis is described of these variables using the OLS system of regression analysis. Single structural equation model of construction tender price level is developed which offer structural explanation of the movements in the index. Indicators of construction price (in real terms) produced by the structural equation were found to be unemployment level, real interest rate, manufacturing profitability, number of registered construction firms, oil crisis, building cost index, construction productivity and construction work stoppages. A Reduced-form model of construction price is developed that utilises simultaneous equation models comprising construction demand, supply and equilibrium models - the reduced-form models being generally regarded as having better predictive power than structural equations. The model is validated by comparing its accuracy with forecasts produced by two leading organisations in U.K. The out-of-sample forecast errors of the reduced-form model are 2.78, 3.58, 4.28 and 5.59 RMSE percent over 0, 1, 2 and 3 quarter forecast horizons respectively, which are better than the Building Cost Information Service (3.32, 5.29, 7.57 and 9.96 RMSE percent) and Davis, Langdon and Everest (3.21, 5.01, 7.16 and 10.41 RMSE percent).
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Strazza, Ernesto (Strazza Silva). "Trend dynamics : a method to improve the analysis, dissemination and forecasting of trends on the Internet." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90247.

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Thesis: S.M. in Management of Technology, Massachusetts Institute of Technology, Sloan School of Management, 2014.<br>43<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 74-76).<br>This thesis provides a new perspective in trend analysis with the acknowledgement of individuals as carriers of trends and susceptible to influence simultaneously by a trend's perceived significance and by external effects (media, peers, society, etc.), which reshape individuals' trend preferences according to the type, direction, and strength of the stimuli received. Internet trends are considered as elements that carry knowledge that can provoke a shift in actors' behavior, altering their preferences to a new direction because they affect individuals' values and beliefs. If trends can change individuals' behavior and conduct, and later, their preference progression, trends also have the capability to accumulate information on what individuals care about, how their inclinations have changed and towards which new interests they are moving. If products and services have specific aspects that trigger selections of the same products and services by unrelated individuals, this will denote the presence of a unique feature or set of features that acts as a gateway of contact. This work proposes the elaboration of actors' and products' networks to be implemented within Internet systems, to improve current technology that captures and displays users' preference evolution and potential trend formations; since people leave traces of their historic behavior, enabling the observance of relations between their current social and product networks, the newly entered and the ones left behind. Each consecutive preferred product contains information of a new assessment of individuals, expressing further necessities, values, aspirations, and desires. If several individuals point to the same products and services, those similar products and services will be the connection element increasing individuals' level of correlation in relation to the amount of products matched. Moreover, if a trend triggers a new preference, the progression of preferences will contain the sequence of drives and external effects or rather, the record of cultural elements that were involved; information that could be used to forecast the probability of positive reactions towards the trend by other similar individuals who were not following it. Because a trend travels through followers, each follower will be part of a virtual network outlined and materialized by the products and services chosen by them. This thesis suggest the idea that a trend is evidenced through the spontaneous sequence of detection, routing and preference from differentiated actors towards the same product or service, because that concordance shows the existence of a broadly valued aspect and a common-connector element that provokes preference agreements among isolated individuals despite their dissimilarities, allowing Web-based companies to develop and observe social-products networks to understand which trends and features have the potential to be successful, which phenomena are affecting preferences and which remaining users are susceptible to increase the diffusion of the trend..<br>by Ernesto Strazza.<br>S.M. in Management of Technology
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Mocke, Barend Adriaan. "An Evaluation of Qualitative Data as Leading Indicators of Trends in the Australian Economy." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/20121.

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The purpose of this study is to evaluate qualitative data as leading indicators of trends in the Australian economy. Economic forecasts usually take the form of quantitative forecasts where economic variables are estimated based on historical trends and relationships between official data as determined by econometric analyses. Economic forecasts based on qualitative techniques, in contrast, focus on the psychological aspects of the relationship between attitudes and economic behaviour. Expectations about future business conditions are formed by taking into account all relevant information, including how policy will affect the economy. Since business surveys collect judgments and expectations for the near future, they are able to detect changes in the cycle earlier that corresponding quantitative statistical series which are usually published after a time lag. Judgments and expectations lead to plans and only when these plans are implemented are they picked up by traditional quantitative statistics. Qualitative data are therefore expected to be early leading indicators of trends in different sectors of the economy. In order to determine the reliability of survey data, it is important to periodically evaluate the data series. It should be noted whether qualitative data follow the same trend as official data and whether turning points in the economy are forecast correctly. It should also be noted whether qualitative data can indeed be used as leading indicators in certain sectors and if forecasts of short-term econometric models improve with the inclusion of the data. In this study we evaluate the forecasting ability of the National Australia Bank’s monthly and quarterly business confidence and condition indices by comparing Australian macroeconomic forecasts that include the data to models without it.The study is composed of two parts. A literature study (chapters 2 to 5) and an empirical study (chapters 6). The first part serves as a background to the empirical study which is the main purpose of the study. In chapters 2 the rational expectations theory is revisited. This is followed by a discussion of the characteristics of Economic Tendency Surveys in chapter 3. Chapter 4 focuses on an empirical overview of previous studies done about the forecasting ability of qualitative data and chapter 5 on the different statistical and econometric techniques applied to evaluate the forecasts. Chapter 6 is the main part of the study where the forecasting ability of qualitative data is evaluated by applying different econometric techniques.Turning point analyses through the application of tests for synchronisation between NAB’s business confidence and conditions indices and the proxy measures of economic activity that we use are positive for both quarterly and monthly data series. Business conditions which reflect current business conditions predict better over the shorter term and business confidence, which reflects expectations about future developments, over the longer term.The value of NAB’s business confidence and conditions is evaluated in VAR models and seems to have only a short-term impact on forecasts. This is also reflected in the impulse response functions which indicated that external shocks to business confidence and conditions will only have a short-term impact on real economic variables. Granger causality Wald tests show that business confidence and conditions Granger causes some economic data series and that all series together Granger causes business confidence and conditions.The forecasting ability of qualitative data is also evaluated in vector auto regressive models by comparing the forecasts of models that include, to models that exclude the data. The models that include the data outperform the models without the data, but in general not significantly so, with the exception of the forecasting of the RBA cash rate up to three months ahead. Qualitative data improve forecasts over the short term up to six months, and therefore prove to be valuable inputs in short term econometric forecast models
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YERRAMILLI, CHINMAYA R. "ANALYSIS OF TRENDS AND PATTERNS IN METAL EVOLUTION." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1109186023.

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Moyo, Vusumuzi. "The generalization ability of artificial neural networks in forecasting TCP/IP network traffic trends." Thesis, University of Fort Hare, 2014. http://hdl.handle.net/10353/d1021127.

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Artificial Neural Networks (ANNs) have been used in many fields for a variety of applications, and proved to be reliable. They have proved to be one of the most powerful tools in the domain of forecasting and analysis of various time series. The forecasting of TCP/IP network traffic is an important issue receiving growing attention from the computer networks. By improving upon this task, efficient network traffic engineering and anomaly detection tools can be created, resulting in economic gains from better resource management. The use of ANNs requires some critical decisions on the part of the user. These decisions, which are mainly concerned with the determinations of the components of the network structure and the parameters defined for the learning algorithm, can significantly affect the ability of the ANN to generalize, i.e. to have the outputs of the ANN approximate target values given inputs that are not in the training set. This has an impact on the quality of forecasts produced by the ANN. Although there are some discussions in the literature regarding the issues that affect network generalization ability, there is no standard method or approach that is universally accepted to determine the optimum values of these parameters for a particular problem. This research examined the impact a selection of key design features has on the generalization ability of ANNs. We examined how the size and composition of the network architecture, the size of the training samples, the choice of learning algorithm, the training schedule and the size of the learning rate both individually and collectively affect the ability of an ANN to learn the training data and to generalize well to novel data. To investigate this matter, we empirically conducted several experiments in forecasting a real world TCP/IP network traffic time series and the network performance validated using an independent test set. MATLAB version 7.4.0.287’s Neural Network toolbox version 5.0.2 (R2007a) was used for our experiments. The results are found to be promising in terms of ease of design and use of ANNs. Our results indicate that in contrast to Occam’s razor principle for a single hidden layer an increase in number of hidden neurons produces a corresponding increase in generalization ability of ANNs, however larger networks do not always improve the generalization ability of ANNs even though an increase in number of hidden neurons results in a concomitant rise in network generalization. Also, contradicting commonly accepted guidelines, networks trained with a larger representation of the data, exhibit better generalization than networks trained on smaller representations, even though the larger networks have a significantly greater capacity. Furthermore, the results obtained indicate that the learning rate, momentum, training schedule and choice of learning algorithm have as much a significant effect on ANN generalization ability. A number of conclusions were drawn from the results and later used to generate a comprehensive set of guidelines that will facilitate the process of design and use of ANNs in TCP/IP network traffic forecasting. The main contribution of this research lies in the identification of optimal strategies for the use of ANNs in forecasting TCP/IP network traffic trends. Although the information obtained from the tests carried out in this research is specific to the problem considered, it provides users of back-propagation networks with a valuable guide on the behaviour of networks under a wide range of operating conditions. It is important to note that the guidelines accrued from this research are of an assistive and not necessarily restrictive nature to potential ANN modellers.
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Marquês, João Carlos de Carvalho Branco Perdigão. "Global and direct solar radiation at surface over Iberian Peninsula: variability, trends and forecasting." Doctoral thesis, Universidade de Évora, 2021. http://hdl.handle.net/10174/29807.

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Besides being the key to Earth's climate, global solar radiation at the surface ( ) is one of the most valuable renewable resources. This way, an adequate knowledge of the solar resource is critical as an assessment for a strategic planning of projects related to the production of solar energy. Therefore, the main goals of this thesis is to analyze past changes and variability of solar radiation fluxes in Portugal and Iberia Peninsula (IP) using observational available measurements, ERA−40 and NCEP/NCAR reanalysis datasets and, predict and characterize the solar radiation at the surface over Iberian Peninsula based on numerical weather prediction models. In a first part, this study is dedicated to the analysis of temporal and spatial variability of  based on ground-based stations, as well as in ERA−40 and NCEP/NCAR reanalysis. Parametric and non-parametric tests are applied to detect trends in both reanalysis and ground-based observations. Cloud cover obtained from reanalysis is also used to examine the possible causes of the observed long-term changes in . In a second stage, is presented an assessment of the  W model at high resolution ( 5 ) against observations and with another configuration. After a bias removal process, a  and cloud cover climatology was obtained for IP (1950−2010 period). Finally, the performance of IFS/ECMWF is evaluated to predict Direct Normal Irradiance (DNI) over Évora city at very short (1 hour) and short term (1 to 3 days), for one year period. It is also described a new methodology to compute DNI attenuation using in situ observational data in order to estimate the transparency of the atmosphere in the absence of cloud cover datasets. To improve IFS/ECMWF outputs is also tested a bias correction methodology; Resumo: A Radiação Solar Global e Direta à Superfície na Península Ibérica: Variabilidade, Tendências e Previsão A radiação solar é um dos recursos energéticos renováveis mais valiosos. Na Península Ibérica (PI) estão em instalação muitos sistemas comerciais e de investigação para o aproveitamento da energia solar. Neste contexto, o conhecimento do fluxo de radiação solar que incide na superfície terrestre e da sua evolução torna-se de extrema importância. Pretende-se com este trabalho estudar a distribuição espacial, a variabilidade e as tendências da radiação solar de pequeno comprimento de onda ( ) à superfície, na PI e em Portugal, a partir de dados observacionais e das reanálises ERA−40/NCEP assim como, prever e caracterizar a radiação com base em modelos de previsão numérica do tempo. Na primeira parte deste estudo, efetua-se uma análise da variabilidade temporal e espacial da radiação  recorrendo a estações terrestres, bem como a dados de reanálise ERA−40 e NCEP/NCAR. Para o efeito utilizam-se testes paramétricos e não paramétricos a fim de detetar tendências nas séries em estudo. A cobertura de nuvens obtida a partir das reanálises é também usada para avaliar as possíveis causas da variabilidade da radiação  observada. Numa segunda etapa do estudo, obteve-se uma climatologia a 5 de resolução da radiação solar à superfície com base em simulações com o modelo regional − , para a PI, e para o período 1950−2010. Os resultados das simulações foram validados recorrendo a estações de observação e a uma outra simulação , com outra configuração, previamente validada. Na construção da climatologia  e de nuvens foi aplicado um método de pós-processamento para remoção do viés. Finalmente, avalia-se o desempenho do modelo IFS, do ECMWF na previsão da radiação DNI a curto e médio prazo, sobre a região. Propõe-se uma nova metodologia para estimar a transparência da atmosfera e testa-se uma metodologia de correção de viés.
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Sundqvist, Lovisa, and Annie Wrang. "Vikten att följa trender - en inköpares dilemma? : En tvärsnittsstudie om beslutsfattandet inom svenska modeföretags inköpsprocesser." Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-173.

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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.
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Liu, Nairui. "Novel applications of modelling techniques to understand and predict global urban air quality trends." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/208434/1/Nairui_Liu_Thesis.pdf.

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The aim of this study was to explore novel applications of modelling techniques to advance analytical methods towards addressing urban atmospheric environment problems and possible solutions. Four selected models known as SVR, STL, WRF, and LSTM were used to investigate: the correlation between social development and air quality, the trend of severe air pollution episodes on a global scale, urban heat island effect mitigation measures, and how to extend the use of air quality monitoring data. This study led to improved analysis of global urban atmospheric environment problems and the formulation of more effective urban air quality management policies.
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Books on the topic "Trends forecasting"

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Hur, Eunsuk, Caroline Hemingray, and Stephen Westland. Fashion Trends and Forecasting. Routledge, 2025. https://doi.org/10.4324/9781003415589.

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Washington (State). Transportation Planning Office., ed. Trends analysis. Washington State Dept. of Transportation, 1998.

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Istituto di ricerche sulla popolazione (Italy)., ed. Italia: Facts and trends in population. Consiglio nazionale delle richerche, Istituto di ricerche sulla popolazione, 1999.

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Sastri, M. N. World demographic trends. Centre for Policy Studies, Gayatri Vidya Parishad, 2011.

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Arkansas. Employment Security Division. Research and Analysis Section., ed. Occupational trends, 1982-1995. Research and Analysis Section, Arkansas Employment Security Division, 1986.

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Goodman, Roger J. Business trends kaleidoscope. Kernow Enterprises, 1994.

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Center, Wisconsin Demographic Services, ed. Wisconsin population trends. Dept. of Administration, Division of Energy and Intergovernmental Relations, Demographic Services Center, 1994.

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Kingkade, Ward. Population trends: Russia. U.S. Dept. of Commerce, Economics and Statistics Administration, Bureau of the Census, 1997.

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Kingkade, Ward. Population trends: Russia. U.S. Dept. of Commerce, Economics and Statistics Administration, Bureau of the Census, 1996.

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Kingkade, William Ward. Population trends: Russia. U.S. Dept. of Commerce, Economics and Statistics Administration, Bureau of the Census, 1997.

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Book chapters on the topic "Trends forecasting"

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Hannagan, Tim. "Forecasting and Trends." In Statistics GCSE. Macmillan Education UK, 1996. http://dx.doi.org/10.1007/978-1-349-13842-5_11.

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Robertson, William. "Forecasting Market Trends." In Tin. Routledge, 2023. http://dx.doi.org/10.4324/9781032694405-10.

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Lechner, Anat, and Leslie Harrington. "Colour forecasting." In Fashion Trends and Forecasting. Routledge, 2025. https://doi.org/10.4324/9781003415589-8.

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Meinel, Christoph, Justus Broß, Philipp Berger, and Patrick Hennig. "Analyzing and Forecasting Trends." In Blogosphere and its Exploration. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-44409-2_15.

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Hur, Eunsuk, Claire Watson, and Meryem Laghmari. "Forecasting industry practices." In Fashion Trends and Forecasting. Routledge, 2025. https://doi.org/10.4324/9781003415589-14.

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Hur, Eunsuk, and Pammi Sinha. "Collaborative fashion forecasting." In Fashion Trends and Forecasting. Routledge, 2025. https://doi.org/10.4324/9781003415589-7.

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Hur, Eunsuk. "Forecasting sustainable fashion futures." In Fashion Trends and Forecasting. Routledge, 2025. https://doi.org/10.4324/9781003415589-5.

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Kang, Zi Young. "Fashion designers' tools for forecasting trends." In Fashion Trends and Forecasting. Routledge, 2025. https://doi.org/10.4324/9781003415589-10.

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Angharad, Sally. "Trends in yarns, fabrics and materials." In Fashion Trends and Forecasting. Routledge, 2025. https://doi.org/10.4324/9781003415589-9.

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Edyvean, Hannah. "Macro-environment analysis and fashion lifestyle trends." In Fashion Trends and Forecasting. Routledge, 2025. https://doi.org/10.4324/9781003415589-4.

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Conference papers on the topic "Trends forecasting"

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S, Samir Ahamed, Radha D, and V. S. Kirthika Devi. "Forecasting Bitcoin Price Trends: Integrated Machine Learning with Market Trends." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914850.

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Touti, Nihal, and Pr Asmâa Alaoui Taïb. "Trends and Patterns in Computational Finance Forecasting." In 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS). IEEE, 2024. http://dx.doi.org/10.1109/icds62089.2024.10756319.

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Altooq, Kawthar Abdulla Ali, Layla Jawad Hasan, Sumathi Kumaraswamy, Shrikant Panigrahi, and Sasikanta Tripathy. "Forecasting Nvidia Stock Trends: An Analytical Approach." In 2024 International Conference on Sustainable Islamic Business and Finance (SIBF). IEEE, 2024. https://doi.org/10.1109/sibf63788.2024.10883878.

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Abdullah, Md, Sajjad Waheed, Monir Morshed, and Md Mahmodul Hasan. "Analyzing and Forecasting Dew Point Temperature Trends in Bangladesh." In 2024 2nd International Conference on Information and Communication Technology (ICICT). IEEE, 2024. https://doi.org/10.1109/icict64387.2024.10839671.

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Preeti, Nikhil Sharma, and Vikash Garg. "Forecasting the Future: Exploring Advanced Trends in Digital Education." In 2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2024. https://doi.org/10.1109/aece62803.2024.10911732.

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Mittal, Harish Kumar, Priya Dalal, Puneet Garg, and Ranjita Joon. "Forecasting Pollution Trends: Comparing Linear, Logistic Regression, and Neural Networks." In 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP). IEEE, 2024. http://dx.doi.org/10.1109/innocomp63224.2024.00074.

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Valli, Latha Narayanan, and N. Sujatha. "Time Series Analysis in Data Science: Forecasting Trends and Anomalies." In 2024 5th International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2024. http://dx.doi.org/10.1109/icosec61587.2024.10722052.

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Zhang, Qiushi, Yantao Liu, Jiancheng Zhou, Boxiang Lei, Yiqun Liu, and Yuhuan Wang. "A Review of Emerging Trends in Wind Power Forecasting Applications." In 2024 The 9th International Conference on Power and Renewable Energy (ICPRE). IEEE, 2024. https://doi.org/10.1109/icpre62586.2024.10768580.

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Armouti, Batool, and Arafat Awajan. "Water Quality Forecasting Using Time Series Techniques." In 2025 International Conference on New Trends in Computing Sciences (ICTCS). IEEE, 2025. https://doi.org/10.1109/ictcs65341.2025.10989394.

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Hrithik, P. M., Rakesh Malhan, Manik Rakhra, Rashmi Mathur, Richa Nandra, and Rakhi Nagpal. "Analysis of Mortality Patterns and Forecasting Future Trends: Actuarial Statistical Approaches." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10841310.

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Reports on the topic "Trends forecasting"

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Fisher, Andmorgan, Taylor Hodgdon, and Michael Lewis. Time-series forecasting methods : a review. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49450.

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Time-series forecasting techniques are of fundamental importance for predicting future values by analyzing past trends. The techniques assume that future trends will be similar to historical trends. Forecasting involves using models fit on historical data to predict future values. Time-series models have wide-ranging applications, from weather forecasting to sales forecasting, and are among the most effective methods of forecasting, especially when making decisions that involve uncertainty about the future. To evaluate forecast accuracy and to compare among models fitted to a time series, three performance measures were used in this study: mean absolute error (MAE), mean square error (MSE), and root-mean-square error (RMSE).
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Zárate-Solano, Héctor M., and Norberto Rodríguez-Niño. Consumer Prices Trends in Colombia: Detecting Breaks and Forecasting Inflation. Banco de la República, 2024. https://doi.org/10.32468/be.1289.

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Colombia’s annual inflation reached 13.3% in March 2023, the highest rate since the implementation of the inflation-targeting regime for monetary policy in 2000. However, some groups within the basket show signs of lower inflation, while others exhibit higher inflation. The persistence of this trend is actively debated, involving analysis of both year-to-year and month-to-month changes in price indices. In this paper, we use a time series methodology to identify shifts in inflation levels based on the 188 price indices that comprise the basket. We classify the trend breaks as positive or negative and aggregate them by tradable and non-tradable, core and regulated, and other CPI groups. Additionally, we employ these trend models, possibly with breaks, to forecast total (bottom-up and middle-up approaches) and group inflation for 2024. Our findings suggest that inflation will decline for most groups by the end of 2024 but will increase for some key groups. Forecast evaluation measures favor using some degree of aggregation, with breaks considerations, for forecasting both annual and monthly inflation.
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Chen, Brian, Hawre Jalal, Hideki Hashimoto, et al. Forecasting Trends in Disability in a Super-Aging Society: Adapting the Future Elderly Model to Japan. National Bureau of Economic Research, 2016. http://dx.doi.org/10.3386/w21870.

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Semerikov, Serhiy, Hanna Kucherova, Vita Los, and Dmytro Ocheretin. Neural Network Analytics and Forecasting the Country's Business Climate in Conditions of the Coronavirus Disease (COVID-19). CEUR Workshop Proceedings, 2021. http://dx.doi.org/10.31812//123456789/4364.

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The prospects for doing business in countries are also determined by the business confidence index. The purpose of the article is to model trends in indicators that determine the state of the business climate of countries, in particular, the period of influence of the consequences of COVID-19 is of scientific interest. The approach is based on the preliminary results of substantiating a set of indicators and applying the taxonomy method to substantiate an alternative indicator of the business climate, the advantage of which is its advanced nature. The most significant factors influencing the business climate index were identified, in particular, the annual GDP growth rate and the volume of retail sales. The similarity of the trends in the calculated and actual business climate index was obtained, the forecast values were calculated with an accuracy of 89.38%. And also, the obtained modeling results were developed by means of building and using neural networks with learning capabilities, which makes it possible to improve the quality and accuracy of the business climate index forecast up to 96.22%. It has been established that the consequences of the impact of COVID-19 are forecasting a decrease in the level of the country's business climate index in the 3rd quarter of 2020. The proposed approach to modeling the country's business climate is unified, easily applied to the macroeconomic data of various countries, demonstrates a high level of accuracy and quality of forecasting. The prospects for further research are modeling the business climate of the countries of the world in order to compare trends and levels, as well as their changes under the influence of quarantine restrictions.
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Semerikov, Serhiy, Hanna Kucherova, and Dmytro Ocheretin. Neural Network Analytics and Forecasting the Country's Business Climate in Conditions of the Coronavirus Disease (Covid-19). Stylos, 2020. http://dx.doi.org/10.31812/123456789/4133.

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The paper proposes an approach to modeling the business climate of the country, which is based on the principles of information transparency, and makes it possible to assess the development trends of the studied indicator in conditions of the COVID-19. This approach has been tested on the example of Ukraine. The results obtained make it possible to analyze the cyclical development of the country's economy with high accuracy and reliability even under quarantine restrictions.
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LaRaine Ingram, Keisha. Applied Sales Predictive Analytics for Business Development. Vilnius Business College, 2024. http://dx.doi.org/10.57005/ab.2024.1.2.

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In the dynamic business environment, leveraging predictive analytics for sales optimization and business development has become crucial for achieving sustained growth. As the e-commerce landscape continues to evolve, many e-businesses must harness the power of predictive analytics to anticipate sales trends and optimize business development strategies. This paper explores the application of sales predictive analytics, focusing on its role in forecasting sales, optimizing resource allocation, and enhancing customer relationship management. The application of predictive analytics in sales forecasting in online marketplace platforms is also explored, through the examination of various predictive models using real-world case studies. By exploring various methodologies and tools, the study illustrates how predictive analytics can be integrated into e-businesses' operations to drive growth, and enhance decision-making, highlighting the transformative potential of analytics in making data-driven decisions, ultimately fostering sustainable growth and competitive advantage. Through the analysis of historical sales data, consumer behaviour patterns, and market trends, predictive analytics provides actionable insights that are crucial for strategic planning and operational efficiency. The paper also addresses challenges and best practices for implementing predictive analytics into the business process of e-businesses.
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Ahm Mehbub, Anwar, Rubal Dua, and Abdulrahman Alwosheel. Approach and Initial Findings in Modeling Future Seaborne Trade and Its Impact on Energy Demand. King Abdullah Petroleum Studies and Research Center, 2024. https://doi.org/10.30573/ks--2024-mp08.

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International shipping contributes approximately 3% of global carbon dioxide (CO2) emissions, prompting the International Maritime Organization (IMO) to aim for net-zero shipping greenhouse gas (GHG) emissions by 2050 to mitigate climate change. Shipping is a critical link between countries for transporting goods, and its future advancements will hinge primarily on customer demand for goods and regional growth trends. This study has a dual objective. First, this work seeks to establish a methodological framework for forecasting the trajectory of future global trade. Second, it aims to utilize the estimated future seaborne trade to understand the impact on energy demand in the shipping sector across three scenarios.
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Noah, Hannelore, and Christopher Wales. Exploring the Development of Mobile Money Markets and Revenue Collection from Digital Financial Services Taxes in Africa. Institute of Development Studies, 2024. http://dx.doi.org/10.19088/ictd.2024.081.

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This policy brief examines the rapid expansion of mobile money and digital financial services, and the recent trend of introducing taxes on these services in Africa. The study focuses on understanding how the market developed in Ghana, Kenya, Nigeria, Tanzania, Uganda, and Zimbabwe, and how the new taxes contribute to tax revenue. The trends in MM and DFS portray a market characterised by resilience and the ability to recover. We show how, after introduction of DFS taxes, there was an initial decrease in usage in the countries studied, but markets typically recovered their momentum. Kenya’s market rebounded quickly after a tax on transaction fees – even faster than Ghana, Uganda, and Tanzania, after introducing taxes on transaction value. For the governments concerned, we show that DFS taxes provide a modest, yet important, revenue stream, generally contributing about 1 per cent to 3 per cent of total tax revenue. Zimbabwe is an outlier, at almost 10 per cent. We also observe that forecasting this revenue remains a challenge in the fast-evolving DFS market. We conclude by offering suggestions for key areas of future research on DFS taxation.
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Navarro, Adoracion, Ma Kristina Ortiz, and Jethro El Camara. How Energy Secure is the Philippines? Philippine Institute for Development Studies, 2023. http://dx.doi.org/10.62986/dp2023.15.

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Energy security is a comprehensive concept, and pursuing it as a developmental goal entails, first and foremost, a clearer understanding of what it means. This study proposes six broad elements in assessing energy security: sufficiency, reliability, resilience, affordability, accessibility, and sustainability. In assessing the energy security situation in the Philippines, the study employs an indicators-based assessment. Based on existing literature, the method involves defining specific energy security indicators (ESIs) corresponding to the six broad elements mentioned, running some calculations, and conducting trend analysis using available data. The analyses of ESI trends over time and comparisons with other Southeast Asian countries reveal notable findings, most of which point to the current energy insecurity of the Philippines. The study then offers recommendations to improve energy security in the country and suggests that each element of energy security be considered an area of future research. As a demonstration of further scrutiny of an aspect of energy security, the study further examines energy sufficiency by forecasting electric power demand and comparing the forecasts with the government’s outlook on generating capacity up to 2028. The results highlight the necessity of timely investments in additional generating capacity and an efficient government permitting system.
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Gelain, Paolo, and Pierlauro Lopez. A DSGE Model Including Trend Information and Regime Switching at the ZLB. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202335.

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This paper outlines the dynamic stochastic general equilibrium (DSGE) model developed at the Federal Reserve Bank of Cleveland as part of the suite of models used for forecasting and policy analysis by Cleveland Fed researchers, which we have nicknamed CLEMENTINE (CLeveland Equilibrium ModEl iNcluding Trend INformation and the Effective lower bound). This document adopts a practitioner's guide approach, detailing the construction of the model and offering practical guidance on its use as a policy tool designed to support decision-making through forecasting exercises and policy counterfactuals.
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