时间序列 Hyndman 结构模式预测

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

Nassif Ghoussoub

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