1. Forecasting an Autoregressive Progress
2. Forecasting a Moving Average Process
3. Forecasting a Seasonal Process
4. Seasonal Adjustment and Forecasting
5. Forecasting with Transfer Function Models
6. Forecasting with Intervention Models
7. Forecasting Multivariate Time Series
8. Preparing Time Series Data for Forecasting
9. Using Macros for Forecasting Tasks
10. Fitting and Forecasting a Linear Model by OLS
11. Testing Forecasting Models for Break Points with Chow Tests
12. Fitting and Forecasting Linear Models with Linear Restrictions
13. Fitting and Forecasting a Linear Model with an AR Error Correction
14. Fitting Linear Models with Heteroscedastic Error Terms
15. Fitting Linear Models with ARCH-GARCH Error Terms
16. Assessing Forecast Accuracy
17. Forecasting Using a Lagged Dependent Variable Model
18. Static and Dynamic Forecasting Using a Lagged Dependent Variable Model
19. Fitting and Forecasting Polynomial Distributed Lag Models
20. Fitting and Forecasting Restricted Polynomial Distributed Lag Models
21. Fitting and Forecasting a Linear System by SUR and ITSUR
22. Testing and Restricting Parameter Estimates in a Linear System Forecast
23. Producing Goodness-of-Fit Statistics for Forecasts of a Linear System o
24. Fitting a Linear System by Instrumental Methods
25. Linear System Diagnostics and Autoregressive Error Correction
26. Creating Forecast confidence Limits with Monte Carlo Simulation
27. Fitting and Forecasting a Nonlinear Model
28. Restricting and Testing Parameters of a Nonlinear Forecasting Model
29. Producing Forecasts Automatically Using the Time Series Forecasting Sys
30. Developing Forecasting Models Using the Time Series Forecasting System
If you didn't get enough, read the backstory from the News and Observer interview with SAS' Bill Marriott.
How to know the boys and girls’ real demands around the world? and how to predict their demands in next Christmas?
How to purchase toys and goodies with a balance of costs and profits? and how to deliver them more efficiently?
There are lots of questions in the list of Santa, CEO of Santa’s Workshop. SAS’s marketing staff held a very creative champion for the coming Christmas. You can watch the interview with Santa in youtube.com, or read the success story about Santa, Santa’s Secret: Magic? No. It’s SAS(R) Business Analytics.
In the latest post, What Makes a Good Business Analyst?, Rajan Chandras cites some soft items from Forrester’s Business Analyst Assessment Workbook:
- Ability to think abstractly, identify patterns, and generate ideas and solutions
- Understanding of when and how to escalate issues or needs
- Understanding of and ability to delivery the appropriate level of detail needed for each task
- Interest in exploring and understanding new concepts and topic areas
- Emotionally invested in the work
- Ability to learn by shadowing stakeholders
- Ability to clearly articulate technology in terms stakeholders can understand
- Understanding of the organizational culture and its impact on processes and projects (this one seems obvious, but the latter phrase is more profound than might seem at first glance)
- Ability to drive a decision analysis and selection process
- Ability to recognize patterns in requirements and categorize them appropriately
What’s more, there are some suggestions by Rajan Chandras himself:
- Know the organization’s external environment: its competitive position, current state of the industry, geographical & social factors, etc.
- Know the organization’s internal environment: its financial position, organization culture, IT maturity, etc.
- Adapt to the needs (your language, dress etc.), but be yourself. Imperfect, yet genuine, is fine; falsity comes through easily, and will destroy your credibility in no time.
No doubt, no boss can reject such a perfect analyst. But I’m afraid these standards are suitable for every professionals. That is to say, they create a model to explain everything. It is too universal to be served as a good filter to select the most proper analysts. She or he may more marketable in any other business line.
Data Mining in Stock Market? Is it crazy? or is it just a hopeless try? Every mentor in mathematics and finance educates us that the stock market is too chaotic and sentimental to use mathematical models. Most of all gift rock scientists are concentrated in the study of interest of rates and fixed income securities. It sounds profitable to use mathematical and statistical models to predict the price of stock, but there are little successfull stories.
I know I might hold some academic doctrines, so I have interest to monitor any effort to try to forecast stock prices using data mining techniques. Some links from a popular data mining blog , Data Mining Research, are listed as follows:
I had an interesting experience analysing some consumer security software Web search results of the 'less than savory' kind. Colorful language is common in the younger generation (18 yrs +/- ~6 yrs) Web sites, and colorful Web sites are just plain common. An innocent search can lead you to places you never intended to go. While you are likely to come across this kind of html data for text mining/text analytics at some point (like I did), I am always pleased to see that Text Miner creates its own segment for this data and I can treat it as noise and continue my analysis focusing on analyzing more useful trends.
It seems like Wordnet might be used to construct synonym lists [for SAS Text Miner] that could map terms "up" to more general synonyms possibly reducing noise and enhancing concept extraction. Has anyone in TM R+D ever considered using Wordnet?Wordnet is a public-domain thesaurus/lexical database for English. It contains synsets or synonym rings that can show all related words to a given word. Since we allow the user to create synonym lists for SAS Text Miner, it seems reasonable to assume that some generic free source of a huge lists of synonyms might be beneficial. And in fact, we have looked at Wordnet before, but found that the reality does not live up to the expectation. In fact, using a generic synonym substitution usually turns out to generate worse results than doing nothing at all.
For why that is we need to look at when synonym substitution is helpful.
Continue reading "When are synonyms useful?"
First prompts are silent.
Subsequent prompts loud and clear.
Now all prompts are heard.
Poem from R&D staff?
Yes. Rhyming sonnets were shakespeare-like complex;
they wrote Japanese haiku, showed as above.
The SAS R&D staff should complete some paper work in defects system before changing a code. They use informal descriptive language(HAIKUUU!) in the early stage. Chris Hemedinger, a senior software engineer at SAS, collected some haikus in his blog to show the humor side of SAS R&D staff. It’s interesting to cite one of the most famous haikus by Matsuo Bashō for comparison:
a frog jumps
the sound of water
I read this verse in W. Bennentt’s popular book, The Book of Virtues, during the bus-to-company time this morning. It’s interesting to read Stevenson’s Treasure Island, of course in Chinese edition when I was young.