From: KU Leuven; ESAT-STADIUS比利时鲁汶大学 ?? How to model real-world multidimensional time series? especially, when these are sporadically observed data. ?? how to describe the evolution of the probability distribution of the data?  ODE dynamics. sporadic…
KDD: Knowledge Discovery and Data Mining (KDD) Insititute: 复旦大学,中科大 Problem: time series prediction; modelling extreme events; overlook the existence of extreme events, which result in weak performance when applying them to real time series. 为什么研究ext…
Problem: TSC, time series classification; Traditional TSC: find global similarities or local patterns/subsequence(shapelet). We extract statistical features from VG to facilitate TSC Introduction: Global similarity: the difference between TSC and oth…
Problem: time series forecasting Challenge: forecasting for non-stationary signals and multiple future steps prediction ?? how to deal with non-stationary datasets?? Introduction one-step prediction problem VS multi-step prediction; multi-step foreca…
Previously in this series: The beta distribution Empirical Bayes estimation Credible intervals The Bayesian approach to false discovery rates Bayesian A/B testing Beta-binomial regression Understanding empirical Bayesian hierarchical modeling Mixture…
Course descriptionWith the continuing advances of geographic information science and geospatialtechnologies, spatially referenced information have been easily and increasinglyavailable in the past decades and becoming important information sources in…
Here is a note of Distance dependent Chinese Restaurant Processes 文章链接http://pan.baidu.com/s/1dEk7ZA5 1. Distance dependent CRPs In the traditional CRP ,the probability of a customer sitting at a table is computed from the number of other customers a…
3.2 考虑电感铜损 可以拓展图3.3的直流变压器模型,来对变换器的其他属性进行建模.通过添加电阻可以模拟如功率损耗的非理想因素.在后面的章节,我们将通过在等效电路中添加电感和电容来模拟变换器动态. Fig 3.3 DC transformer 让我们来考虑下Boost电路中电感的铜损.实际电感器会表现出两种功率损耗:(1)由导线电阻导致的铜损:(2)由磁芯中的磁滞和涡流导致的磁芯损耗.图3.5给出了使用电感器与电阻\(R_{L}\)串联的结构描述了适合电感器铜损的模型.所以实际电感就是包含理想…
5 DyREP:Learning Representations Over Dynamic Graphs link:https://scholar.google.com/scholar_url?url=https://par.nsf.gov/servlets/purl/10099025&hl=zh-CN&sa=X&ei=kIF4YrmVJ-OM6rQPxfOKUA&scisig=AAGBfm3I4EpwNkRLc5xhuaLEs47V0XWOzA&oi=schola…
PROBLEM: OmniAnomaly multivariate time series anomaly detection + unsupervised 主体思想: input: multivariate time series to RNN ------> capture the normal patterns -----> reconstruct input data by the representations ------> use the reconstruction pr…