Xixi Li
@XixiLi07
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Postdoc at Tsinghua University. Time series forecasting, causal inference, deep learning
Manchester, England
Joined February 2019
"My main job is to develop the musical abilities of machines to enable functions such as music fingerprinting, personalized recommendation, and voice tuning," shares #TsinghuaRen alumna Guo Yichen, a musical algorithm engineer at @Huawei who also enjoys playing in her own band.
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Do you want to learn more about forecast combinations? Read this outstanding new review paper by @Xia0qianWang @robjhyndman @f3ngli @YanfeiKang published at the International Journal of Forecasting. https://t.co/0Bxfo7EpHL
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Thanks for mentioning it. In this study, we propose a new approach called DeepVARwT that employs deep learning methodology for maximum likelihood estimation of the trend and the dependence structure at the same time.
DeepVARwT: Deep Learning for a VAR Model with Trend https://t.co/hIqykUxrkZ by @XixiLi07 et al. #LongShortTermMemory #Estimator
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To improve computation efficiency in current big data settings，instead of using information criteria for ETS model selection，we directly predict appropriate ETS model component forms for a given time series using pretrained classifiers.
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Our boy, Freddy, went missing yesterday at Bathampton, Bath, UK. Please, help us find him.
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Wonderful experience at Cambridge machine learning summer school and many thanks to @CambridgeEllis @Cambridge_Uni @jmhernandez233 @PetarV_93 for their organization and lectures.
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Joint work with @YanfeiKang @YunBai16 We associate the contents in M5 Kaggle with those top solutions in M5 competition with topic modeling and social network analysis.
Exploring the social influence of Kaggle virtual community on the M5 competition https://t.co/IdgMZek14M by @XixiLi07 et al. #LossFunction #MachineLearning
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Can't wait to read it and learn from it.
In our "Understanding Forecast Reconciliation" EJOR article, we offer a Combined Conditional Coherent (CCC) forecasting approach that combines forecasts across hierarchical levels using equal weights. 50-day free access link:
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Here are my top-three tips on academic publishing: 1) Work on what excites you. 2) Understand your skills and limitations; utilise your network. 3) Don't be afraid to submit your work (even if the result is rejection); use the comments from the review team to improve your paper.
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The first snow in UK at Christmas Day. Merry Christmas and Happy New Year to u.
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When I introduce my research in the party, someone else also say 'can you predict the stock market and we all get rich'.
My 2.5 hour tutorial at ACML2020 on Forecasting for Data Scientists is now online:
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Good.
Congrats to @XiaoqianWang7 and @XixiLi07 from https://t.co/ianfKm4RDW for w㏌n㏌g the awards at #ISFConf2020． A very big thank you to the #ISFConf2020 organizers and the spo㎱ors. @gathana1 @DrHongTao @PamStroud Also very grateful to our collaborators! @robjhyndman and @fotpetr
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#ISFConf2020 wraps up for us with a well-attended "deep learning for forecasting" workshop together with @lostella material is here: https://t.co/eXH9ail42w including a notebook with #gluonts & #pytorch
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Great work!
"Forecasting: theory and practice" This unique article offers an encyclopedic review of the theory and practice of forecasting. 74 authors from 22 countries, 241 pages, 1912 references. Direct download link of the preprint version:
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Great time presenting a tutorial on Deep Generative Models at the Deep Learning for Science School ( https://t.co/Yjcmhecs60) at Berkeley! Video: https://t.co/8HuuIxrvKV Slides: https://t.co/qsbJI4FFN5
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