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DTSTART;TZID=Asia/Seoul:20251226T100000
DTEND;TZID=Asia/Seoul:20251226T120000
DTSTAMP:20260422T195142
CREATED:20251026T141349Z
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SUMMARY:N-BEATS: Neural basis expansion analysis for interpretable time series forecasting - Jinwoo Hyun
DESCRIPTION:In this talk\, we discuss the paper “N-BEATS: Neural basis expansion analysis for interpretable time series forecasting” by B. Oreshkin et al.\, ICLR\, 2020. \nAbstract \nWe focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties\, being interpretable\, applicable without modification to a wide array of target domains\, and fast to train. We test the proposed architecture on several well-known datasets\, including M3\, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets\, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year’s winner of the M4 competition\, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that\, contrarily to received wisdom\, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally\, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.
URL:https://www.ibs.re.kr/bimag/event/journal-club-jinwoo-hyun/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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