Data Analysis & Simulation

Archive for the ‘Risk’ Category

EasyFit Used for Probabilistic Currency Forecasting

Monday, February 21st, 2011

Because risk and uncertainty are a part of literally all areas of our life, with the finance being one of the most important areas, scientifically based risk management methods are gaining more and more popularity among the finance industry professionals. Currency fluctuations affect all businesses dealing with multiple currencies, so having at least some degree of certainty about the future exchange rates can be a significant success factor for any international enterprise. A wide range of currency forecasting methods have been developed, however, not many of them can pretend to be reliable in the long run: most algorithms only work for a short period of time, and need to be tweaked as the market conditions change.

Brijen Hathi, a Research Fellow at the Planetary & Space Sciences Research Institue, performs his own research in the field and publishes the results in the Currency Forecasting Blog. The forecasting methodology employed by Mr. Hathi is in part based on the same techniques used in probabilistic risk analysis. Like with most modern forecasting methods, in this approach, he uses historical data to predict the future, but the big difference here is that he also assigns specific probabilities to the predictions. For example, for a US-based company doing business in the UK, it doesn’t really matter what the exact GBP/USD exchange rate is going to be during the next 30 days, as long as it stays within a specific interval with a high probability (95% or more). Recently Mr. Hathi has published an article highlighting the use of EasyFit to model pricing probability of the Pound Sterling versus the US Dollar from historical data. It is fascinating to see how EasyFit is being used in (what we believe) a truly scientific approach to data analysis, and we hope to see new developments in this area soon.

EasyFit Used to Improve the Forecasting of Software Project Status

Monday, November 29th, 2010

The software development community struggles with a way to identify if their projects are on-schedule given the inherent risks of constant invention that inevitably has elements of uncertainty and risk. Current practice is for developers to estimate a software project, and attempt to consider (up-front) all variations to get a viable estimate of time and cost. This process is laborious, and even with due rigor, project slip when the realization that estimates versus actual times fail to match. This leads to costly project overruns and lack of trust in future estimates.

As part of the Agile movement for software development, we think there is a better way and are championing the use of Monte-Carlo simulation as a ways of assessing likely progress and dealing with delays as early as possible… read the full case study

Will Cloud Computing Make Risk Analysis More Economically Efficient?

Monday, November 1st, 2010

What is Cloud Computing?

For some time now, there has been a lot of buzz around cloud computing – the relatively new computing paradigm in which the resources, software, and information are shared on the computer clusters and delivered to the users on demand through the Internet. The idea behind cluster computing is not new: if your applications require a lot of computing resources or impose very strict reliability requirements which cannot be met by a single personal computer or a server, you can link a group of computers into a cluster that will provide a much better performance.

Why Not Build a Cluster Yourself?

Building and maintaining a computer cluster in your organization may have some downsides, such as large upfront investments into technology infrastructure.and high running costs. Of course, there are companies that will do the job of building and managing a computer cluster for you, but anyway, the bottom line is: depending on how loaded your cluster is going to be, it may or may not be economically feasible for your company to run it on-site. For instance, if you need to quickly perform a very CPU-intensive calculation (e.g. render a complex 3D scene), but only once a day, chances are the cluster will not pay off.

And here’s where cloud computing comes into play: you can have access to great computing resources, pay only as you use them, and not worry about the underlying technology infrastructure. These factors combined can provide a great economic benefit, and some major Internet players, including Amazon and Google, are already offering cloud computing platforms for those who want to make their businesses more efficient.

Is Cloud Computing a Good Fit for Risk Analysis?

As one might guess, not just any kind of application can be efficiently run on the cloud. Because at the core of a cloud is a number of computers linked into a cluster, it is very good at processing a large number of independent tasks, such as requests to a web server. That might be the reason why the cloud computing platforms offered by Amazon and Google are mostly used to run websites.

If you consider risk analysis, it looks like an ideal application to be run on the cloud: an input model of several megabytes that can be easily sent to the cloud, a need for huge computational resources to quickly perform Monte Carlo simulation and distribution fitting, sometimes a need for a lot of storage to hold intermediate results, and relatively small-sized analysis results that can be sent back to the users as text and graphics. Add to that the ever-increasing complexity of risk models used across various industries, causing analysts to wait for hours while their simulations are running, and you have a potentially good opportunity to make risk analysis more economically efficient.

To perform further research in this field, we have partnered with Supportex, a technology services company based in Czech Republic, Europe. Supportex has some good experience providing a cloud computing platform for solving problems much more complex than just processing requests to a web server, that is why we have decided to rely on their hardware infrastructure and domain knowledge to run some test applications and see if cloud computing can be of real help in the field of risk analysis.

EasyFit Used by NASA to Improve Monte Carlo Risk Simulation Models

Tuesday, February 10th, 2009

On April 17 2005, the Millstone nuclear generating plant in Connecticut shut down when a circuit board monitoring a steam pressure line short-circuited. “Tin whiskers” – microscopic growths of the metal from soldering points on a circuit board – were blamed for causing the problem. These whiskers are comprised of nearly pure tin, and are therefore electrically conductive.

Field failures attributable to tin whiskers have cost individual programs many millions of dollars each. As a result, manufacturers of high-reliability systems are forced to use Monte Carlo simulation models to decide whether the use of tin poses an acceptable risk in a given application.

Recently a group of NASA scientists lead by Karim J. Courey, a Principal Engineer with the Orbiter Sustaining Engineering Office, Lyndon B. Johnson Space Center, used our distribution fitting software EasyFit to better understand the underlying process and develop a probability model that can be used to improve existing Monte Carlo risk simulation models… read the full case study

Need To Deal With Risk and Uncertainty in Your Software Applications?

Tuesday, December 23rd, 2008

Lately we have received a couple messages from customers asking if it’s possible to use the Monte Carlo simulation and distribution fitting features of EasyFit in their own software applications. The short answer is yes, but these features are limited to calculating some distribution functions in Excel VBA. There’s currently no way to run simulations, fit distributions to data, perform goodness of fit tests, or use distribution functions from C#, C++, VB.NET, and other programming languages.

To fill the gap, we are considering to create a Simulation & Probabilistic Analysis Software Development Kit (SPA SDK) for software developers who need to deal with risk & uncertainty in their applications, but don’t have time or expertise to design and implement the required features on their own. We already have in place the tried and true technology that’s a basis for our distribution fitting products EasyFit and EasyFitXL, so creating an SDK would be possible in a short period of time.

Since we have had only a few requests for an SDK, we would like to know whether you would be interested in such kind of product. Below is our vision for the SDK – you are welcome to express any thoughts or specific requirements you might have. Please feel free to contact us and we will take your input seriously.

Update: The free beta version of the SDK is now available for download – please click here for details.

What is a Simulation & Probabilistic Analysis Software Development Kit (SPA SDK) ? (more…)

EasyFit Used for Environmental Fate and Risk Assessment

Tuesday, November 4th, 2008

Since 1991, the European Union has been promoting the use of numerical models to assess the environmental fate and risk of pesticides. Recently a group of scientists from the Catholic University and the Marche Polytechnic University (Italy) in association with Informatica Ambientale, the Milan-based research and computer science company, developed a tool that integrates one of the pesticide fate models with GIS software. Several distribution fitting software products were tested to introduce distribution functions in the risk assessment study, and EasyFit was selected as the most appropriate tool for analyzing annual mean pesticide concentration and determining the most suitable distribution… read the full case study

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