Rob J Hyndman 时间序列#
30 Jun 2015 Exploring the feature space of large collections of time series
26 Jun 2015 Seminar Exploring the boundaries of predictability: what can we forecast, and when should we give up?
25 Jun 2015 Seminar Automatic algorithms for time series forecasting
**23 Jun 2015 Seminar **MEFM: An R package for long-term probabilistic forecasting of electricity demand
19 Jun 2015 Seminar Probabilistic forecasting of peak electricity demand
**08 Jun 2015 Working paper **STR: A Seasonal-Trend Decomposition Procedure Based on Regression
04 Jun 2015 Working paper Probabilistic time series forecasting with boosted additive models: an application to smart meter data
01 Jun 2015 Working paper Large-scale unusual time series detection
26 May 2015 Seminar Visualization of big time series data
22 May 2015 Seminar Probabilistic forecasting of long-term peak electricity demand
For the next few weeks I am travelling in North America and will be giving the following talks.
19 June: Southern California Edison, Rosemead CA.“Probabilistic forecasting of peak electricity demand”.
23 June: International Symposium on Forecasting, Riverside CA.“MEFM: An R package for long-term probabilistic forecasting of electricity demand”.
**25 June: Google, Mountain View, CA.“Automatic algorithms for time series forecasting”.
**26 June: Yahoo, Sunnyvale, CA.“Exploring the boundaries of predictability: what can we forecast, and when should we give up?”
**30 June: Workshop on Frontiers in Functional Data Analysis, Banff, Canada.“Exploring the feature space of large collections of time series”.
The Yahoo talk will be streamed live.
I’ll post slides on my main site after each talk.
Useful tutorials
There are some tools that I use regularly, and I would like my research students and post-docs to learn them too. Here are some great online tutorials that might help.
ggplot tutorial from Winston Chang
Writing an R package from Karl Broman
Rmarkdown from RStudio
Shiny from RStudio
git/github guide from Karl Broman
minimal make tutorial from Karl Broman
**"诺亚方舟实验室李航：深度学习还局限在复杂的模式识别上" **网页链接
【诺亚方舟实验室李航：深度学习还局限在复杂的模式识别上】华为诺亚方舟实验室主任@李航博士 接受CSDN的采访，分享人工智能、机器学习技术在诺亚的应用状况，以及他对这些技术趋势的认识。他认为深度学习目前还停留在“复杂的模式识别”层面上，但会极大地推动人工智能的进步。
专家
Functional Data Analysis
Functional Data Analysis
Functional data analysis in shape analysis
Workshop at BIRS: Frontiers in Functional Data Analysis Reports from Workshops in 2015
