2012年4月23日星期一

Matlab R2010


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《MATLAB X个实用技巧—MATLAB中文论坛精华总结》

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Matlab程序外包: Matlab程序项目有偿交易

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使用MATLAB来预测新兴市场中的金融危机
www.21jrr.com 发布时间:2010-05-18 16:11 来源:未知 


摘要: 1997 年,马来西亚、菲律宾、泰国、印尼等国开始的金融危机迅速在世界范围内蔓延,造成了巨大的损失。经济学家Paul McNelis 着手研究是否现代的研究方法和工具可以预测这样的金融危机,从而减少它们带来的损失。 Pa……

1997 年,马来西亚、菲律宾、泰国、印尼等国开始的金融危机迅速在世界范围内蔓延,造成了巨大的损失。经济学家Paul McNelis 着手研究是否现代的研究方法和工具可以预测这样的金融危机,从而减少它们带来的损失。 21jrr.com
Paul McNelis 将他的研究重点放在了印度尼西亚,1997 年秋天,印尼的卢布价值急剧下跌,国内对美元的需求到达了一个前所未有的水平,即使后来印尼政府从国际货币基金组织获得了230 亿美元的贷款,情况也并没有得到控制。McNelis 在美国国际发展委员会的技术协助下,在印尼的印尼银行开始他的研究,在这项雄心勃勃的计划中,McNelis 始终在利用MATLAB 这个强大的工具以及其中的Excel Link , Statistics ,Optimization , control system, Neural Netowrk, System Identification 工具箱。
天下金融网

挑战
McNelis 着手分析印尼13 年来每个月对货币的需求量,这期间包含了金融危机这段时间。经济学家通常使用的线性分析方法和误差修正并不能适应现在的情况。他需要去确定一种非常有效的方法来分析这些累积以后产生的大量数 据,同时,他还要尽力减少数据中的巨 大波动使预测结果产生错误的可能性, 例如金融危机期间市场对美元的高需 求。 因为MATLAB 具有强大的功能,易于 使用,并且可以处理超大规模的数据 集,McNelis 选择了MATLAB 作为研究 的工具。从方法从来讲,他相信通过结 合线性模型和神经网络模型可以获得更 为准确的结果。McNelis 对神经网络的 优点解释道:估算不仅仅是对数据的顺 序化处理,从输入X 就可以得到Y , 而 是采用并行的方式来处理,隐含的层面 中的多个神经元将对输入同时进行处 理。 21jrr.com
解决方法
McNelis 分析过程中,核心的内容是和 Pittsburgh 大学的John Duffy 教授一起开 发的遗传算法。在开发这个算法的过程 中,他们使用了MATLAB 中的 Statistics 工具箱,同时还使用了矢量化 函数来加快数据的处理速度。"从算法中 获得的系数可以作为更通用的本地搜索 方法的起始值",McNelis 说道. 他在它的 搜索方法中使用了Optimization 工具箱 中的非线性最小化函数 在收集完数据以后,McNelis开始使用传 统的线性模型来过的一个他可以得到的 最好的结果,然后从这个模型中得到输 入值来构建神经网络 McNelis说,在定义神经网络的过程中, 他首先从一个简单的网络开始,例如, 隐含层面中只有3-4个神经元,然后使用 混合的方式来训练这个网络,开始的时 候利用遗传算法为神经网络来寻找一个 系数集,随后利用这些系数转向一个非 线性梯度递减的方法 McNelis使用了Neural Network工具箱中 的前馈结构来将输入和输出关联起来, 他为此解释道:尽管他曾经在不同的金 融应用中试验过不同的神经网络结构, 但是他认为最好的还是有一个隐含层面 的前馈体系结构。他为隐含层中的每一 个神经元使用了工具箱中的log sigmoid 激活函数。输入被传送到隐含层,通过 log sigmoid激活函数进行挤压,最终,神 经元做为线性组合被传送到输出层。 McNelis 通过使用了一个基于时变的 GARCH模型来模拟1997年11月到12月印 尼国内货币的戏剧性贬值,从而增加了 神经网络的预测能力。 该模型具有很强的预测能力。McNelis开 发的神经网络模型同传统的线性模型相 比,可以获得相当高的精确度,并且 GARCH的使用使这个模型的预测能力更 进一步。
21jrr.com

结论
印尼银行现在正在使用McNelis的模型来 预报货币需求量以及预测通货膨胀率,借 此来增强他们抵御所必需面对的通货膨胀 压力。McNelis认为他的模型同时可以被 用来监控汇率的波动,来做为危机预测的 一个有效的早期预警系统。 McNelis博士执教于Georgetown大学,他 基于Matlab的经济分析技术,是金融界领 先的分析技术,得到了亚洲和南美洲中央 银行和以及金融组织的广泛使用。他同时 从如何减轻经济转型中给大众带来的金融 困境的角度,教授学生使用基于MATLAB 的分析方法。
天下金融网

“ MATLAB功能非常强大,并且易于使用,我有信心,印度尼西亚银行能够应用
MATLAB来做为他们的金融危机早期预警系统. ”
――Dr. Paul McNelis, Georgetown University 天下金融网

http://scottlocklin.wordpress.com/2009/05/08/choose-your-weapon-matlab-r-or-something-else/

Locklin on science

Choose your weapon: Matlab, R or something else?

Posted in tools by Scott Locklin on May 8, 2009
As a data sort of guy, I use three programming tools on a daily basis, or at least every week. One is Lush a version of lisp. The other is Matlab. Lastly, there is the R project.
I don’t want to use three tools for dealing with data, but it’s actually necessary right now. I don’t think it will be necessary forever.
Lush is my general purpose programming language. It’s insanely great. Parts of it are wonky and slow, and parts of it are broken or missing, but it’s a lisp, it’s fast where I need it, and I like it a lot. More on this in a future entry. I use Lush for speed and original research. If there are no complex algorithms like what I need written in Matlab or R, I might as well write them in Lush. Lush is a high level language with low level speed when you need it. It would be perfect if it had more libraries. The only thing I may potentially like better is OCaML/F#, and frankly, I find the type inferencer there to get in the way more than it helps. If they made an OCaML where you could turn the type safety off most of the time, that would be better. Or, I could just be like everyone else and use Python or Java for this sort of thing. Not that there is anything wrong with that.
Matlab would be my second choice for hacking out original research. Why Matlab? Matlab is reasonably fast, but one of the main value adds is that it is extremely intuitive if you’ve used Fortran or C, and if you don’t know how to do something, the help system is very informative. Matlab code is also extremely well supported. The debugger, profiler and editor are all excellent; some of the best I’ve used. Sure, someone will argue that they have more powerful debugger, but Matlab’s is the most handy I’ve yet used. I don’t need to read a manual to use it; I just use it. Sure, emacs is way better than the Matlab editor, but it isn’t as handy as Matlab’s editor. You can use Matlab to do just about anything. I’ve used it to code up embedded systems using xPC target and Real Time Workshop. I’ve used it to code up trading systems, from data feed to broker interface. I’ve embedded it in Excel for end users. I’ve deployed it in Enterprise software used by Fortune 100 companies. It’s amazingly useful stuff, especially if you have the proper toolbox to accomplish your tasks. You can build reasonably good numeric software with it as long as you don’t need fancy “programmy” features like concurrency. If Matlab had a way of making fast compiled code, it would be close to perfect for the type of thing I do. I wouldn’t bother with Lush any more, except when I was trying to write interpreter type things. Alas, Matlab’s way of doing this is to write code for your time critical pieces in C, and embed it into your code in a fairly laborious process. The only real drawbacks to Matlab are speed, plotting and expense.
What is R good for then? Well, R is free, so many academics use it to share their latest econometric or machine learning software with everyone else. As such, just about everything statistical under the sun exists in R. And it’s free! What is not to love. Well, sadly, there is plenty not to love about R. First off, there is speed. R doesn’t seem to have anything that makes it inherently slow for an interpreted language: it should be comparable to Matlab in this regard. But it’s slow enough that most people do their heavy work in other languages. Most of the modules written for it have most of the code written in C or Fortran. This is somewhat true of Matlab also, and for the same reasons, but Matlab has a trivial way of telling you what you need to speed up, so R will always end up slower in practice. Second there is debugging. R is hard to debug. First off, it doesn’t drop you into an interactive top level the way Matlab (or Lush, or Python or anything where you write Real Programs) does. That sucks a lot, and removes a bunch of the utility of using an interpreted language. Oh, sure, there is a debugger, but it is buggy, poorly documented, and doesn’t work in the simple way that Matlab’s does. Thirdly, there is the syntax. Personally, I like the syntax; it’s a lot like OCaML. But most people don’t. What is more; the help system is very close to worthless if you’re trying to remember a simple command. People may say this is unfair, as I am just not used to R, but the fact is, I’ll never get as used to it as Matlab, and neither will anyone else. Oh, it’s OK for finding packages you want if you can think of the right keyword for them. But compared to Matlab, or even something like Lush, its online help is pretty worthless. Fourthly: for programming, while it should be better than Matlab in many ways, I haven’t ever seen a legible R program which was over 100 lines. I don’t know how they manage this. Part is doubtless the IDEs are rather bad. I don’t know anyone who claims they can write good, large pieces of software for R. I once asked a guy how he wrote big pieces of software, and he said, “very carefully.”
This sounds pretty bad, but there are solid reasons to use R. For one thing; it’s free. There is a lot to be said for free. Among other things, if you want to give some code away for others to play with, R is going to be a better vehicle than distributing raw C or a matlab package. For another thing, it has a tremendous amount of work done on various hard numeric problems, and installation is trivial: just press a button. Want to wire the latest AdaBoost up to your database, and plot some nice results: pretty easy in R. I might be able to do all this in Matlab, with the correct packages and so on, but in R, it’s the work of seconds. Another thing: it’s a lot easier to make fancy plots in R than it is in Matlab. Matlab’s plotting utility is from the dark ages. It’s insanely bad. You can abstract some of its badness away with objects, but … you shouldn’t have to. Finally, for interacting with data, R wins. Matlab’s matrix paradigm makes it easy to use, but data.frames are more powerful.
Here’s how my decision tree works. When I first heard about Benford’s law, I decided it was simple enough; I’d hack it out in Lush. I did. It worked, and I fiddled with it. Then I realized that goodness of fit to Benford’s distribution might be nice. I had chi-squared distributions already coded up in Lush, and some curve fitting stuff … but wiring it all together, then fiddling with the plotting routines: ugh. So, google informed me that some nice statistician had done all that work for me in R. So I used R. Probably, someone did it in Matlab also (actually, someone did), but it’s a pain to fire up my Windows laptop with Matlab on it, so I just went with R. That’s what R is good for. At some point, I’ll get Lush talking to R, at which point I may cease using Matlab unless someone pays me to do so. It will never be as slick as Matlab, and I will miss all the great user productivity features that Matlab offers, but it will get the job done better and quicker, I think.
I use the cheat sheets in R a lot, for lack of a better help system, so if you want to fool around with it:
A cheat sheet
A better cheat sheet
Other R documents
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  1. R and data « Erehweb’s Blog said, on May 27, 2009 at 5:42 am
    [...] and data By erehweb My fellow bloggers John and Scott have posted recently about the free statistical programming language R. How does it compare to an [...]
  2. adam said, on August 6, 2009 at 2:11 pm
    Hi – Just found this blog and I’m really enjoying your writing.
    In regards to R’s debugger – I’ll agree its poorly documented, but I haven’t found it to be that bad. Have you tried “options(error=recover)” and “withCallingHandlers(fun(), warning=function(c) recover())”? Also, I haven’t tried it (and it may be what you were talking about as ‘buggy’) but the debug package (install.packages(debug)) looks promising in terms of what you want.
    • Scott Locklin said, on August 6, 2009 at 8:47 pm
      The last time I tried debug, it crashed my system and made me sad. I was going to try it again for this article, but I didn’t see it available for R2.9/OS-X at the time. It seems to be available now. Fiddling for 30 seconds, I’m remembering issues like, “I had to remember to call mtrace() on everything that might crash.” Still, it’s better than what I was doing before.
      I don’t like it as much as Matlab’s debugger (or what Lisp does by default), but it comes a lot closer to making me happy -maybe it will grow on me. Thanks for pointing it out.
  3. Win-Vector Blog » Survive R said, on September 29, 2009 at 6:12 am
    [...] at Win-Vector LLC appear to like R a bit more than some of our, perhaps wiser, colleagues ( see: Choose your weapon: Matlab, R or something else? and R and data ). While we do like R (see: Exciting Technique #1: The “R” language ) we also [...]
  4. Arthur said, on September 29, 2009 at 11:21 pm
    Great blog post. Still waiting for my work’s IT group to approve my download of R, but I have experience with MATLAB and SPlus.
    My absolute favorite environment for rapid and powerful coding is Dyalog APL. The language allows really powerful abstraction, and the IDE’s debugger is the best I’ve ever seen. You can step backwards and forwards, and add/modify code without having to exit debug mode. Most Dyalog users find themselves writing code within the debugger.
    • Scott Locklin said, on September 30, 2009 at 12:39 am
      I had a very brief and scary encounter with APL in learning linear algebra when I was 20 or so. It certainly looks powerful, and has a great pedigree, but trying to read it: ouch. Probably Lisp looks the same way to the uninitiated. While Lisp lacks decent IDE’s (beyond emacs + SLIME, which is admittedly pretty good), you can certainly sling code in the debugger: its one of Lisp’s superpowers. Anyhow, I’m pretty sure at some point somebody is going to pay me to sling K, which is a sort of vectorized APL variant by one of the original authors.
      http://en.wikipedia.org/wiki/K_(programming_language)
      R is crazy frustrating at times, but with the helpful “cheat sheets” you can get a lot done. I’m sure your Splus will serve you well.
  5. Rob Brown said, on October 5, 2009 at 5:47 pm
    I love R, but quite frankly, the best modeling language/environment is Analytica published by Lumina. Here’s a review I wrote about it last year for OR/MS Today:
    http://lionhrtpub.com/orms/orms-6-08/frswr.html
  6. lew burton said, on October 9, 2009 at 6:04 am
    Both of those look neat. The connection with Babcock and Brown is quite funny.
  7. Helmingstay said, on October 21, 2009 at 11:06 pm
    options(error=recover) and options(warn=2) are the most helpful (non-default) settings that ive found for run-of-the-mill errors. Just to clarify, by “crash” do you mean the R interpreter crashes? Ive heard many more complaints about matlab “giving up the ghost” than R.
    An ongoing weakness in R seems to be an odd/poor set of default choices (as with the options above). Google “stringsasfactors” and witness a long list of novice users ready to do violence to their computers. R’s factor-handling has brought me to the verge of tears, only to discover a single pithy sentence in the docs that clarifies all.
    In short, R’s public relations team isnt likely to win any awards today, tomorrow, or ever…
    • Scott Locklin said, on October 21, 2009 at 11:38 pm
      I think I’ve encountered “stringsasfactors” before. I’ve been living in the R debugger for the last few days. When I say it crashes, I mean crash as in crashes R; sometimes with pagefault, sometimes it gets stuck in some kind of wacky ESS REPL->somewhere else loop, probably with the debugger’s TK bits. Either way, I lose whatever I was doing with a kill -9.
      Other fun: keeping track of which libraries you have loaded from where. I found a fun “bug” in my code which couldn’t be reproduced on different installs of R; apparently the old version of XTS (or ZOO, I never figured out which was at fault) allowed you to subset pretty sloppily. New version requires everything be just so. Finding out which lib R was pointing to … any of 3-4 in Framework or my home directory: insanity. In the end, I’m going to have to maintain an R distribution along with my code, because the libraries change so much underneath the code, I can’t rely on CRAN to do it for me for anything resembling software. Shoulda done it in Lush.

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