[Gretl-users] Tricks 'n Treats in Time Series Analysis

At one point in the myth, Gretel (and Hansel) consume the sweet (eye) candy house (architecture). Could this be an aplicable metaphor for Statistical Learning Theory applied to Time Series Prediction? A chocolate covered bicameralism? Happy Halloween and thanks for your help. I intend to apply gretl (possibly R too) for the purpose of forecasting models of the Foriegn Exchange. The platform from wich the Foriegn Exchange time series data is observed and recorded is the MT4, Meta Trader 4 platform from Metaqoutes. Scripts may be created in the metaqoutes language as well as algorithmic trading rules. The menu path of the MT4 platform would be under Tools then History (also F2), and from there various currency pairs (EUR/USD) as well as time period (one hour chart, 4 hour chart, daily chart) are available to export as .csv, .htm and .pm files. MT4 also offers tick by tick data via a DDE (dynamic data exchange) link. The MT4 platform is one that is used the most often in this industry and it is available with no cost. Data is freely available from most retail Forex brokers when you sign up for a practice account, in case you would like free data to play with. I typically use Open Office calculator .ods files as my default spread sheet. The gretl handbook talks about stacked time series and the commnad SETOBS on pg.23-24 and then up to Ch.5 on pg.30 it mentions other useful commands for stacked time series such as: UNITVAR, TIMEVAR, FREQ, STRCUTURE, STACK, etc. It's my understanding that gretl expects data to be arranged by observation and each row represents an observation. The time series data I am working with is the exchange price of currency pairs in multiple time frames (minute chart, hour chart, daily, weekly). For example at 12PM noon on October, 31, 2008 the exchange price for a Euro dollar in U.S. dollars is $1.27 (expressed as EUR/USD 1.2732). Further the data from the MT4 is formated in rows as date, time, Opening price, High, Low, Closing price, volume. This is the standard formatting and I am still simply working on organizing the data from the MT4 platform to the gretl platform. I was able to put the daily closing price all in a row and import it into gretl but I still need to figure out the SETOBS command to get the proper period. I also need to have a better understanding of how best to stack this time series data (hour charts within daily charts within weekly). My inital question then is for suggestions on how to stack these time series. I'm seeking suggestions on strategies for nesting this data with gretl for the purpose of forecasting models. The books that I am using as guides for this project are "Time Series Prediction: Forecasting the Future and Understanding the Past" by Andreas Weigend and Neil Gershenfeld and the book "Statistical Learning Theory" by Vladimir Vapnik. There are several other books that led me here such as "Chaos Under Control" by Peak and Frame. I've noticed patterns on all scales from some spread sheet experiments with multiple currency pairs and I want to investigate further with gretl. I have not been formally educated past a college associate level and I am learning on my own. Help and direction is greatly appreciated. The stacking question I have is in reference to observing these self similar patterns from multiple time frames. How may I investigate these self similar properties within a stacking type of architecture in time series analysis is the best way I may frame my question without going into further detail. Time Series tricks 'n treats David