DOCSLIB.ORG

Forecasting Fashion Consumer Behaviour Using Google Trends

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

social sciences $€ £ ¥ Article Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends Emmanuel Sirimal Silva 1,* , Hossein Hassani 2 , Dag Øivind Madsen 3 and Liz Gee 4 1 Centre for Fashion Business and Innovation Research, Fashion Business School, London College of Fashion, University of the Arts London, 272 High Holborn, London WC1V 7EY, UK 2 Research Institute of Energy Management and Planning, University of Tehran, Tehran 1417466191, Iran; [email protected] 3 Department of Business, Marketing and Law, School of Business, University of South-Eastern Norway, Bredalsveien 14, 3511 Hønefoss, Norway; [email protected] 4 Centre for Fashion Business and Innovation Research, Fashion Business School, London College of Fashion, University of the Arts London, 272 High Holborn, London WC1V 7EY, UK; [email protected] * Correspondence: [email protected] Received: 22 February 2019; Accepted: 29 March 2019; Published: 4 April 2019 Abstract: 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. Keywords: Google Trends; fashion; forecast; neural networks; singular spectrum analysis; big data 1. Introduction The emergence of big data and advances in big data analytics led to the creation of Google Trends (Choi and Varian 2012), a tool and source for analysing big data on web searches across the globe. Given that 70% of luxury purchases are estimated to be influenced by online interactions (D’Arpizio and Levato 2017), Google Trends has the potential to play a pivotal role in the developments of big data in fashion. Fashion consumers are actively using Google, looking for ideas, finding the best designs, and buying with a tap (Boone 2016). In 2016 alone, Google was receiving more than 4 million search queries per minute from 2.4 billion internet users and was processing 20 petabytes of information per day (Wedel and Kannan 2016). Data analytics on such online behaviour lets Google predict the next big fashion trend (Bain 2016) with Google’s Online Retail Monitor indicating that in 2018, fashion has Soc. Sci. 2019, 8, 111; doi:10.3390/socsci8040111 www.mdpi.com/journal/socsci Sustainability 2019, 11, x FOR PEER REVIEW 2 of 24 Soc. Sci. 2019, 8, 111 2 of 23 lets Google predict the next big fashion trend (Bain 2016) with Google’s Online Retail Monitor indicating that in 2018, fashion has seen the highest growth in searches, boosted by overseas shoppers seen(especially the highest from the growth EU) inseeking searches, access boosted to UK by brands overseas online shoppers (Jahshan (especially 2018). Such from trends the EU)are positive seeking accessfor the to fashion UK brands industry online in (Jahshan the UK 2018 which). Such is trendsexperiencing are positive considerable for the fashion uncertainty industry with in theBrexit UK whichlooming is experiencingon the horizon. considerable uncertainty with Brexit looming on the horizon. Google TrendsTrends is is a gooda good example example of howof how big databig da canta becan exploited be exploited and visualised and visualised in a user-friendly in a user- style.friendly The style. term The big term data big itself data struggles itself struggles to find ato universal find a universal definition definition and it can and mean it can different mean different things tothings different to different people people (Marr (Marr2015; 2015;Hassani Hassani and Silva and Silva2015b 2015b).). Nevertheless, Nevertheless, most most researchers researchers agree agree on buildingon building upon upon the threethe three defining defining dimensions dimensions of Big of Data Big (3Vs) Data as(3Vs) introduced as introduced by Laney by(2001 Laney): volume, (2001): varietyvolume, and variety velocity. and velocity. Figure1 below Figure summarises 1 below summaris the 3Vses to the give 3Vs the to reader give the an reader indication an indication on how big on datahow wasbig data initially was thought initially to thought be expanding. to be expanding. Today, the definition Today, the of bigdefinition data has of evolved big data (much has evolved like the size(much of biglike data the size which of hasbig onlydata gottenwhich biggerhas only as predictedgotten bigger by Varian as predicted 2014) and by nowVarian includes 2014) 5Vs,and withnow additionalincludes 5Vs, Vs beingwith additional veracity (which Vs being accounts veracity for (which the quality accounts of the for data) the andquality value of (whichthe data) accounts and value for analytics(which accounts on data) for (Hassani analytics and on Silva data) 2018 (Hassani). and Silva 2018). Figure 1. The 3Vs of big data (Soubra (Soubra 20122012).). It is no secret thatthat GoogleGoogle Trends areare increasinglyincreasingly influencinginfluencing businessbusiness decision-makingdecision-making in a variety of industries (see, for example, YuYu et al. 2019;2019; Siliverstovs and and Wochner Wochner 2018;2018; ZhangZhang et et al. al. 20182018)) given its ability to act as a leading indicator for forecasting key variables of interest. The fashion industry tootoo cancan benefit benefit from from the the exploitation exploitation of Googleof Google Trends Trends for forecastingfor forecasting fashion fashion variables, variables, from predictingfrom predicting future future purchase purchase decisions, decisions, to determining to determining the effectiveness the effectiveness of marketing of marketing campaigns campaigns and forecastingand forecasting online online consumer consumer brand brand engagement. engagement. Moreover, Moreover, there is there a need is fora need more for conclusive more conclusive research whichresearch evaluates which evaluates whether bigwhether data in big the data form in of the Google form of Trends Google can Trends help predict can help actual predict sales actual for fashion sales brands.for fashion Whilst brands. finding Whilst the answerfindingto the this answer problem to this is beyond problem the is scope beyond of our the research, scope of there our research, is reason tothere believe is reason that thisto believe could that be the this case, could since be the evidence case, since suggests evidence that Googlesuggests searches that Google can predictsearches other can typespredict of other economic types activity of economic such as activity real estate such sales as real and estate prices sales (Wu and Brynjolfssonprices (Wu and 2015 Brynjolfsson), exchange rates2015), ( Bulutexchange 2018 rates), UK (Bulut cinema 2018), admissions UK cinema (Hand admissions and Judge (Hand2011), stockand Judge market 2011), volatility stock (Hamidmarket andvolatility Heiden (Hamid 2015), inflationand Heiden expectations 2015), inflation (Guzman ex 2011pectations) and tourist(Guzman arrivals 2011) (Bangwayo-Skeete and tourist arrivals and Skeete(Bangwayo-Skeete 2015). In addition, and Skeete there 2015). could In beaddition, several ther alternatee could research be several avenues alternate which research are waiting avenues to bewhich explored are waiting not only to from be explored a fashion not design, only buying from a and fashion merchandising design, buying perspective, and merchandising but also from Soc. Sci. 2019, 8, 111 3 of 23 a fashion management perspective (see, for example, Madsen 2016), particularly in evaluating the success of marketing and social media campaigns. We subscribe to Gordon’s (2017) view that Google Trends can be a metric for online consumer behaviour, as more than 75% of the world’s internet searches are conducted on Google (Net Market Share 2019). Therefore, we believe that the fashion industry should consider relevant Google Trends as ‘fashion consumer Google Trends’ which according to McDowell(2019) can enable brands to identify consumer patterns and profit from them. Here, it is worthwhile to define Google Trends for the reader. In brief, as one of the largest real time datasets currently available (Rogers 2016), recording Google search data from 2004 to present (Choi and Varian
Recommended publications
  • Overview of Fashion

    Overview of Fashion

    Chapter01 vrv Overview of Fashion 1.1 UNDERSTANDING FASHION: INTRODUCTION AND DEFINITION Fashion has become an integral part of contemporary society. It is an omnipresent aspect of our lives and is one of the focal topics of the print and electronic media, television and internet, advertisements and window displays in shops and malls, movies, music and modes of entertainment etc. Fashion is a statement that signifies societal preferences created by individual and collective identities. The key to its core strength lies in its aspiration value, which means that people aspire to be fashionable. Fashion travels across geographical boundaries, history influencing and in turn, influenced by society. Though the term 'fashion' is often used synonymously with garment, it actually has a wider connotation. A garment is not fashion merely because it is worn. To become fashion, a garment has to reflect the socio-cultural ethos of the time. As a generic term, fashion includes all products and activities related to a 'lifestyle' - clothes, accessories, products, cuisine, furniture, architecture, mode of transportation, vacations, leisure activities etc. Fig 1.1 Evening gowns by Sanjeev Sahai Creative Director of Amethysta Viola Fashion has emerged as a globally relevant area of academic study which includes various aspects of fashion design, fashion technology and fashion management. Due to the wide range of human and social aspects within its ambit, it is also a topic of scholarly study by sociologists, psychologists and anthropologists. 1 01 v r v Its multi-faceted nature leads to numerous interpretations. For an average person, fashion could generally refer to a contemporary and trendy style of dressing being currently 'in' and which is likely to become 'out' by the next season or year.
  • The Use of Trend Forecasting in the Product Development Process

    The Use of Trend Forecasting in the Product Development Process

    THE USE OF TREND FORECASTING IN THE PRODUCT DEVELOPMENT PROCESS C TWINE PhD 2015 THE USE OF TREND FORECASTING IN THE PRODUCT DEVELOPMENT PROCESS CHRISTINE TWINE A thesis submitted in partial fulfilment of the requirements of the Manchester Metropolitan University for the degree of Doctor of Philosophy Department of Apparel / Hollings Faculty the Manchester Metropolitan University 2015 Acknowledgements The completion of this research project owes a great deal of support and encouragement of my supervisory team, Dr. David Tyler, Dr. Tracy Cassidy as my adviser and the expert guidance of my Director of Studies Dr. Ji-Young Ruckman and Dr. Praburaj Venkatraman. Their experience and commitment provided inspiration and guidance throughout those times when direction was much needed. I am also indebted to all those who participated in the interviewing process which aided the data collection process and made this research possible. These included personnel from the trend forecasting agencies Promostyl, Mudpie, Trend Bible and the senior trend researchers from Stylesight, and Trendstop. The buyers and designers from the retailers Tesco, Shop Direct, Matalan, River Island, Mexx, Puma, Bench, Primark, H&M, ASOS and Boohoo. Thank you to my lovely family, David and Florence for their support and encouragement when I needed it most. i Declaration No portion of the work referred to in this thesis has been submitted in support of an application for another degree or qualification of this or any other university or institution of learning. Copyright© 2014 All rights reserved No part of this thesis may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior permission of the author.
  • Trend Analysis and Forecasting the Spread of COVID-19 Pandemic in Ethiopia Using Box– Jenkins Modeling Procedure

    Trend Analysis and Forecasting the Spread of COVID-19 Pandemic in Ethiopia Using Box– Jenkins Modeling Procedure

    International Journal of General Medicine Dovepress open access to scientific and medical research Open Access Full Text Article ORIGINAL RESEARCH Trend Analysis and Forecasting the Spread of COVID-19 Pandemic in Ethiopia Using Box– Jenkins Modeling Procedure Yemane Asmelash Introduction: COVID-19, which causes severe acute respiratory syndrome, is spreading Gebretensae 1 rapidly across the world, and the severity of this pandemic is rising in Ethiopia. The main Daniel Asmelash 2 objective of the study was to analyze the trend and forecast the spread of COVID-19 and to develop an appropriate statistical forecast model. 1Department of Statistics, College of Natural and Computational Science, Methodology: Data on the daily spread between 13 March, 2020 and 31 August 2020 were Aksum University, Aksum, Ethiopia; collected for the development of the autoregressive integrated moving average (ARIMA) 2 Department of Clinical Chemistry, model. Stationarity testing, parameter testing and model diagnosis were performed. In School of Biomedical and Laboratory Sciences, College of Medicine and Health addition, candidate models were obtained using autocorrelation function (ACF) and partial Sciences, University of Gondar, Gondar, autocorrelation functions (PACF). Finally, the fitting, selection and prediction accuracy of the Ethiopia ARIMA models was evaluated using the RMSE and MAPE model selection criteria. Results: A total of 51,910 confirmed COVID-19 cases were reported from 13 March to 31 August 2020. The total recovered and death rates as of 31 August 2020 were 37.2% and 1.57%, respectively, with a high level of increase after the mid of August, 2020. In this study, ARIMA (0, 1, 5) and ARIMA (2, 1, 3) were finally confirmed as the optimal model for confirmed and recovered COVID-19 cases, respectively, based on lowest RMSE, MAPE and BIC values.
  • UC Riverside UCR Honors Capstones 2016-2017

    UC Riverside UCR Honors Capstones 2016-2017

    UC Riverside UCR Honors Capstones 2016-2017 Title GTWENDS Permalink https://escholarship.org/uc/item/41g0w0q9 Author Asfour, Mark Publication Date 2017-12-08 Data Availability The data associated with this publication are within the manuscript. eScholarship.org Powered by the California Digital Library University of California GTWENDS By Mark Jeffrey Asfour A capstone project submitted for Graduation with University Honors October 20, 2016 University Honors University of California, Riverside APPROVED ______________________________ Dr. Evangelos Christidis Department of Computer Science & Engineering ______________________________ Dr. Richard Cardullo, Howard H Hays Chair and Faculty Director, University Honors Associate Vice Provost, Undergraduate Education Abstract GTWENDS is an online interactive map of the United States of America that displays the locations of trending Twitter tweets, Google Search trends, and Google Hot Trends topics. States on the map are overlaid with a blue color where Twitter trends originate and a red color where Google trends originate with respective opacity levels varying based on the levels of interest from each website. Through the use of web crawling, map-reducing, and utilizing distributed processing, this project allows visitors to have an interactive geographic visual representation of current national social media topic activities. Visitors can use GTWENDS to learn about social media trends by observing where trends originate from, comparing the contrasts and similarities between trends from Twitter and Google, understanding what types of events trigger mass social media sharing, and much more. ii Acknowledgements I have worked on and developed GTWENDS with my classmates, Mehran Ghamaty (University of California, Riverside Spring 2016) and Jacob Xu (University of California, Riverside Spring 2016), during our senior design project class, CS 179G Databases Spring 2016, under the supervision of my professor and mentor, Professor Evangelos Christidis.
  • Towards an Orchestration of Forecasting Methods to Devise Strategies for Design” – Also Comes from a Place of Personal Motivation

    Towards an Orchestration of Forecasting Methods to Devise Strategies for Design” – Also Comes from a Place of Personal Motivation

    TOWARDS AN ORCHESTRATION OF FORECASTING METHODS TO DEVISE STRATEGIES FOR DESIGN by Priyanka Sharma APPROVED BY SUPERVISORY COMMITTEE: ___________________________________________ Dr. Maximilian Schich, Chair ___________________________________________ Dr. Kimberly Knight ___________________________________________ Dr. Matt Brown ___________________________________________ Dr. Frank Dufour Copyright 2018 Priyanka Sharma All Rights Reserved TOWARDS AN ORCHESTRATION OF FORECASTING METHODS TO DEVISE STRATEGIES FOR DESIGN by PRIYANKA SHARMA, B.Des, MA DISSERTATION Presented to the Faculty of The University of Texas at Dallas in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY IN ARTS AND TECHNOLOGY THE UNIVERSITY OF TEXAS AT DALLAS August 2018 ACKNOWLEDGMENTS I am greatly appreciative of the many individuals who provided support and encouragement for my work through the process of writing this dissertation. Above all, I would like to thank my doctoral committee for their continuous guidance, critical feedback, and timely advice in the past few years. I am especially indebted to my advisor Dr. Maximilian Schich, whose invaluable insight, unique perspective, persistence for perseverance provided me with the inspiration and motivation to work towards this dissertation. I thank Dr. Dufour for being the relentless source of optimism and his faith in my efforts. His support and guidance made me hopeful of being capable of ingenuity and left me intellectually stimulated. I express my sincere gratitude toward Dr. Kim Knight for her trust in me and enabling me to continue this journey. I am indebted to Dr. Matthew Brown, who during the entire course of writing this dissertation helped me immensely with the structural aspects of this dissertation and guided me towards practical and critical milestones in my research.

  • Finding the Face of Your Data 

    Finding the Face of Your Data There’s been an explosion in data assets Growth of the “digital universe”1 Data overload in context2 40,000 1 EB = 1 billion gigabytes x70 30,000 20,000 10,000 Exabytes 2009 2020 IDC estimates that “tagged” information accounts for only about 3% The amount of information generated by humanity during the first of the digital universe, with analyzed information at 0.5%. The value day of a baby’s life today is equivalent to 70 times the information of big data technology lies in exploring the “untapped pools.” contained in the Library of Congress. Enterprise expectations are as big as the data Big data spending forecast by component3 50 Compute 31% CAGR Storage 40 Networking 30 Infrastructure Software SQL Database Software 20 NoSQL Database Software Application Software 10 Professional Services Billion Dollars Xaas 2011 2012 2013 2014 2015 2016 2017 According to a Wikibon study, big data spend will shift from infrastructure and middleware to value-add services and software during the next five years. Infrastructure, middleware, and technical services will likely become increasingly commoditized as they mature and common standards are adopted. We also note that this study did not include the costs associated with the business and domain experts’ time – a critical element of actionable insight. Taking advantage of data requires new tools ... Traditional and non-traditional value in data Data diving Pattern finding In taking advantage of new data assets – The other side of that same coin, from internal, external, structured, and seeking and patterning for previously unstructured data – and analytics tools, unasked and unanswerable questions, the most common form of value is realized is less common, but potentially more through exploiting deeper detail for new important to the enterprise.
  • Unit 4 Lesson 1

    Unit 4 Lesson 1

    Unit 4 Lesson 1 What is Big Data? Resources Name(s)_______________________________________________ Period ______ Date ________________ Unit 4 Lesson 01 Activity Guide - Big Data Sleuth Card Directions: Web Sites: ● With a partner, select one of the tools in the list to the right. 1. Web archive http://www.archive.org ​ ● Determine what the tool is showing. 2. Measure of America http://www.measureofamerica.org/maps/ ​ ● Find the source of the data it allows you to explore. 3. Wind Sensor network http://earth.nullschool.net/ ​ ● Complete the table below. 4. Twitter sentiment https://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/ ​ 5. Alternative Fuel Locator http://www.afdc.energy.gov/locator/stations/ ​ Website Name What is this website potentially useful for? What ​ kinds of problems could the provided information be used to solve? Is the provided visualization useful? Does it provide insight into the data? How does it help you look at a lot of information at once? How could it improve? Where is the data coming from? Check for “About”, “Download”, or “API”. You may also need to do a web search. ● Is the data from one source or many? ● Is it static or live? ● Is the source reputable? Why or why not? ● Add a link to the raw data if you can find one. Do you consider this “big” data? Explain your reasoning. 1 Unit 4 Lesson 2 Finding Trends with Visualizations Resources Unit 4 Lesson 2 Name(s)_______________________________________________ Period ______ Date ___________________ Activity Guide - Exploring Trends What’s a Trend? When you post information to a social network, watch a video online, or simply search for information on a search engine, some of that data is collected, and you reveal what topics are currently on your mind.
  • Using Artificial Intelligence to Analyze Fashion Trends

    Using Artificial Intelligence to Analyze Fashion Trends

    Using Artificial Intelligence to Analyze Fashion Trends Mengyun (David) Shi Van Dyk Lewis Cornell University, Hearst Magazines [email protected] ABstract Analyzing fashion trends is essential in the fashion industry. Current fashion forecasting firms, such as WGSN, utilize the visual information from around the world to analyze and predict fashion trends. However, analyzing fashion trends is time-consuming and extremely labor intensive, requiring individual employees' manual editing and classification. To improve the efficiency of data analysis of such image-based information and lower the cost of analyzing fashion images, this study proposes a data-driven quantitative abstracting approach using an artificial intelligence (A.I.) algorithm. Specifically, an A.I. model was trained on fashion images from a large-scale dataset under different scenarios, for example in online stores and street snapshots. This model was used to detect garments and classify clothing attributes such as textures, garment style, and details for runway photos and videos. It was found that the A.I. model can generate rich attribute descriptions of detected regions and accurately bind the garments in the images. Adoption of A.I. algorithm demonstrated promising results and the potential to classify garment types and details automatically, which can make the process of trend forecasting more cost-effective and faster. Keywords: artificial intelligence, fashion, trend analysis Note: this paper provides an introduction of how A.I. can Be adopted for fashion trend analysis. If you are working or studying in the fashion industry. this is the right paper for you to get started. However, if you are an advanced computer/research scientist, we suggest you read the fashionpedia project that we collaBorate with Google AI directly: https://fashionpedia.github.io/home/index.html 1.
  • Public Health & Intelligence

    Public Health & Intelligence

    Public Health & Intelligence PHI Trend Analysis Guidance Document Control Version Version 1.5 Date Issued March 2017 Author Róisín Farrell, David Walker / HI Team Comments to [email protected] Version Date Comment Author Version 1.0 July 2016 1st version of paper David Walker, Róisín Farrell Version 1.1 August Amending format; adding Róisín Farrell 2016 sections Version 1.2 November Adding content to sections Róisín Farrell, Lee 2016 1 and 2 Barnsdale Version 1.3 November Amending as per Róisín Farrell 2016 suggestions from SAG Version 1.4 December Structural changes Róisín Farrell, Lee 2016 Barnsdale Version 1.5 March 2017 Amending as per Róisín Farrell, Lee suggestions from SAG Barnsdale Contents 1. Introduction ........................................................................................................................................... 1 2. Understanding trend data for descriptive analysis ................................................................................. 1 3. Descriptive analysis of time trends ......................................................................................................... 2 3.1 Smoothing ................................................................................................................................................ 2 3.1.1 Simple smoothing ............................................................................................................................ 2 3.1.2 Median smoothing ..........................................................................................................................
  • Comfort in Clothing: Fashion Actors and Victims. in Miller, M

    Comfort in Clothing: Fashion Actors and Victims. in Miller, M

    CROSS, K. 2019. Comfort in clothing: fashion actors and victims. In Miller, M. (ed.). Fashion: ID; proceedings of 21st International Foundation of Fashion Technology Institute (IFFTI) 2019 conference: fashion ID (IFFTI 2019), 8-12 April 2019, Manchester, UK. Manchester: Manchester Metropolitan University [online], pages 284-297. Available from: http://fashioninstitute.mmu.ac.uk/ifftipapers/paper-168/ Comfort in clothing: fashion actors and victims. CROSS, K. 2019 This document was downloaded from https://openair.rgu.ac.uk Comfort in Clothing: Fashion Actors and Victims Karen Cross Robert Gordon University [email protected] Abstract Fashion psychology is an emerging discipline, recognising the potential of clothing to enhance well-being in an era when mental health issues are increasing in the Western world. Well-being is important to the individual and on a wider societal level, with the Office for National Statistics monitoring the well-being of UK inhabitants and the World Health Organisation stating that depression will be the most common health issue in the world by 2030. As comfort is a key aspect of well-being, this study explores meanings associated with comfort and discomfort in everyday, non-elite clothing. Comfort in clothing can by physical, physiological and psychological, and the psychological comfort gained from clothing is identified in literature as under-researched. Psychological theory was explored, revealing individuals perform multiple identities, dependent on the reaction of others and filtered by previous, lived experience. Fashion was found to be a recognized method of communicating identity in the social space and research suggests the physical response to psychological constructs or meanings associated with certain garments can be used to change or enhance mood.
  • Initialization Methods for Multiple Seasonal Holt–Winters Forecasting Models

    Initialization Methods for Multiple Seasonal Holt–Winters Forecasting Models

    mathematics Article Initialization Methods for Multiple Seasonal Holt–Winters Forecasting Models Oscar Trull 1,* , Juan Carlos García-Díaz 1 and Alicia Troncoso 2 1 Department of Applied Statistics and Operational Research and Quality, Universitat Politècnica de València, 46022 Valencia, Spain; [email protected] 2 Department of Computer Science, Pablo de Olavide University, 41013 Sevilla, Spain; [email protected] * Correspondence: [email protected] Received: 8 January 2020; Accepted: 13 February 2020; Published: 18 February 2020 Abstract: The Holt–Winters models are one of the most popular forecasting algorithms. As well-known, these models are recursive and thus, an initialization value is needed to feed the model, being that a proper initialization of the Holt–Winters models is crucial for obtaining a good accuracy of the predictions. Moreover, the introduction of multiple seasonal Holt–Winters models requires a new development of methods for seed initialization and obtaining initial values. This work proposes new initialization methods based on the adaptation of the traditional methods developed for a single seasonality in order to include multiple seasonalities. Thus, new methods to initialize the level, trend, and seasonality in multiple seasonal Holt–Winters models are presented. These new methods are tested with an application for electricity demand in Spain and analyzed for their impact on the accuracy of forecasts. As a consequence of the analysis carried out, which initialization method to use for the level, trend, and seasonality in multiple seasonal Holt–Winters models with an additive and multiplicative trend is provided. Keywords: forecasting; multiple seasonal periods; Holt–Winters, initialization 1. Introduction Exponential smoothing methods are widely used worldwide in time series forecasting, especially in financial and energy forecasting [1,2].
  • Apache Hadoop & Spark – What Is It ?

    Apache Hadoop & Spark – What Is It ?

    Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Apache Hadoop & Spark { What is it ? Demo Joseph Allemandou JoalTech 17 / 05 / 2018 1 / 23 Hello! Hadoop { Spark Overview Joseph Allemandou J. Allemandou Generalities Hadoop Spark Demo 2 / 23 Plan Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark 1 Generalities on High Performance Computing (HPC) Demo 2 Apache Hadoop and Spark { A Glimpse 3 Demonstration 3 / 23 More computation power: Scale up vs. scale out Hadoop { Spark Overview J. Allemandou Scale Up Scale out Generalities Hadoop Spark Demo 4 / 23 More computation power: Scale up vs. scale out Hadoop { Spark Overview Scale out J. Allemandou Scale Up Generalities Hadoop Spark Demo 4 / 23 Parallel computing Hadoop { Spark Overview Things to consider when doing parallel computing: J. Allemandou Partitioning (tasks, data) Generalities Hadoop Spark Communications Demo Synchronization Data dependencies Load balancing Granularity IO Livermore Computing Center - Tutorial 5 / 23 Looking back - Since 1950 Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Demo Figure: Google Ngram Viewer 6 / 23 Looking back - Since 1950 Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Demo Figure: Google Ngram Viewer 6 / 23 Looking back - Since 1950 Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Demo Figure: Google Ngram Viewer 6 / 23 Looking back - Recent times Hadoop { Spark Overview J. Allemandou Generalities Hadoop Spark Demo Figure: Google Trends 7 / 23 Same problem, different tools Hadoop { Spark Overview J. Allemandou Generalities Supercomputer Big Data Hadoop Spark Demo Dedicated hardware Commodity hardware Message Passing Interface 8 / 23 MPI Hadoop { Spark Overview J. Allemandou Generalities C / C++ / Fortran / Python Hadoop Spark Demo Low-level API - Send / receive messages a lot to do manually split the data assign tasks to workers handle synchronisation handle errors 9 / 23 Hadoop + Spark Hadoop { Spark Overview J.