User Manual - Stock Fusion
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1. Returns the minimum forecast length Default is 1 int MaxForecastLen Returns the maximum forecast length Default is MAX_INT int MinNuminputs Returns the minimum number of inputs Default is 1 int MaxNumInputs Returns the maximum number of inputs Default is MAX_INT int MinNumOutputs Returns the minimum number of outputs Default is 1 int MaxNumOutputs Returns the maximum number of outputs Default is MAX_INT Of all listed functions only one must be implemented to ensure the full functionality of the algorithm within the framework Namely CalculateForecasts all other functions can be safely omitted from implementation if their default functionality is sufficient and complies to the particular algorithm Implemented functions from the above list must be compiled into standard windows DLL and exported from it properly It can be done in any programming language The resulting DLL module must then be placed into the ModelDrv subfolder of the original application folder tree 21 The application must be restarted in order to recognise the newly supplied algorithm 222. This information can be interactively queried from the user through SettingsForm function int ModelReport int iReportLen char pReportBuffer int BufLen void pModelBuffer int iParamLen void pModelParameters int iPathLen const char pResPath Describe model based on info stored in buffer while it was calculated If not supplied no reporting option is available for the model ReportLen Input The size of buffer to hold the report pReportBuffer Output The buffer where the function must store the report Html or text formats are expected iBufLen Input The size of buffer to hold the model info pModelBuffer Input The buffer which holds the model info previously stored during model calculation iParamLen Input The size of buffer to hold the model parameters pModelParameters Input The model settings parameters PathLen Input The size of buffer to hold the resource path pResPath Input The url path to the web server which processes the current model Developers can upload their own graphics etc to store on the server and use in their custom reporting int SettingsForm int iFormLen char pFormBuffer int iBufLen void pModelBuffer int iParamLen void pModelParameters int iPathLen const char pResPath const char pServerName Prepares the custom web form to ask user for model settings Returns the acquired info in the pModelParameters buffer for later use by CalculateForeca
3. Tune of menu Model If some values are not available for estimation then the corresponding cells remain empty Selection of multiple regression ALASKA AIR GROUP Taking into account the influence that all other time series in this model may have on this particular time series we were able to select the following multiple regression model to forecast the future estimates of this series YHagta x a x a Xy where y ALASKA AIR GROUP the order of the regression equation is 7 9 and the individual coefficients of the equation are contained in the following table f time lag coefficient regressor a g 0 000000 aoe General constant y F Each table has row header which displays observation index Header can be hidden through submenu Table in menu Format Model description This form displays the technical details of the model used for series prediction Details can change from model to model depending on exact solution chosen by Aura for particular time series If the model is multivariate then its 13 description contains hyperlinks to other series which influence the current series If the model is multilayered then this page contains also the to the contained child models Data exchange formats Aura offers the rich choice of data exchange formats which allow easy integration with the other applications export and import of data Clipboard support You c
4. Model E ALABAMA PWR CO fy ALADDIN KNOWLEL ff ALAMO GROUP INC ff ALAMOSA PCS HLD f ALANCO TECHNOL Lal gt E D D z m o z 5 EEEE H ES Z202 5525 Pres e cogo DZE EnX S ALBEMARLE CORP 2 040000000 bo EE E aS Do a or 5 2 E S pe Ed ALBERTSONS INC E ALCAN ALUM LTD E ALCATEL ALCIDE CORP ST st ll attractive charts detailed tables and very sophisticated multilevel model descriptions linked with intuitive hypertext links designed not to get you lost in this abundance of models You can instantly import tremendous amounts of data both from the desktop applications and external databases analyze them with one mouse click and store to disk with unique speed in native format as well as export results for the further use in many formats Lea lsAeomme pe aiz2 ae 20 model W Graph Num If you are software developer not interested in these visual features then even more you can benefit from Aura power and flexibility because it offers you many levels of simple and effective open user APIs to access custom data sources incorporate additional forecasting models store results in the external databases of your choice or just embed the whole engine into your custom application and enjoy all its power through the customized web interface which best fits your needs Major Features Unique forecasting technology based on fractal metanet architecture ca
5. Non seasonal ARIMA models for individual forecasting of medium and long time series e Adaptive selection of seasonal ARIMA models e Multiple linear regression analysis with confidence intervals for the future responses e Selection of the regression model using a forward stepwise algorithm e Leaps and bounds algorithm for determining a number of best regression subsets from a full regression model e Special set of multivariate tests which assist combining the above methods into a most effective model While most of the above algorithms are widely known and available the real power of our solution proceeds from the ability to automatically merge these computational methods into a flexible and effective forecast model Using Aura Simple definitions Statistical model The primary goal of Aura is adaptive forecasting of the time series The forecast is based on statistical model of the series Statistical model is the main structural unit of Aura Each model consists of input output and forecasting algorithm Input of model Input of model consists of arbitrary number of conditionally grouped observations of any nature and frequency Output of model Output of the model contains the series of forecasts calculated for each input of the model Each output represents the time series derived from input by means of forecasting algorithm Forecasting algorithm Is the function that transforms the input values into
6. forecast The system automatically chooses the algorithm that provides the best forecast Master model Master model is the main model which holds any other models corresponding to current working session Master model has its name which expresses its user friendly meaning You can change this name any time through command Name from menu Model AutoRelation Analysis Each model can be saved to the separate permanent storage and reloaded from it later we Model fa Below is the list of series present in this model gt ALADDIN KNOWLEDG Big models can be loaded in parts by ALAMOSA PCS HLDG demand saving computer memory and e ALANCO TECHNOLOG ALARIS MED INC allowing to work with huge models e ALASKA AIR GROUP e ALASKA COMMUNICA ALBANYNILCORE You can explore master model structure in ALBEMARLE CORP y several ways The easiest of them is using ALREDTO CULMED O model tree Model tree The structure of the model is shown in a special window which usually resides on the left side of application screen and has the caption Structure of model This window reveals the Selec whole model structure in a multilevel tree and guides you through all model components If you accidentally closed that window you can ALASI display it by command Model Tree from Taking in menu View a Structure of model eix f ALABAMA PWR CO H ALADDIN KNOWLEL H ALAMO GROUP INC ff
7. framework registers any dll that conforms to the specification in unified algorithm repository and selectively uses them in each case to provide the best forecast The sample of such interface in C is provided below Functions are split in groups for easier navigation Most functions in dll can be missed In that case the framework replaces them with default implementations which do trivial default processing Unified specification of interfaces to the expert shell and algorithm library Globally identify algorithm in algorithm library const char AlgorithmName Return user friendly name of algorithm implemented in the dll If not implemented model name returned to user as valid string Unknown Forecasting core functions int CalculateForecasts int iNumInputs int iInputLen double pInputMatrix int iNumOutputs int iForecastLen double pOutputMatrix double pVarianceMatrix double pDateTime char pSeriesNames int iBufLen void pModelBuffer int iParamLen char pModelParameters Calculate forecasts If this function is omited all forecasts are considered not existing This function must be implemented for algorithm to give any reasonable results iNumInputs Input Number of input series iInputLen Input The length of each input series plnputMatrix Input The array of size exactly Numinputs iInputLen containing all input series in the following order First InputLen elements contain first series nex
8. values for timestamps of input observations to indicate that corresponding points were missed by algorithm NaN timestamps of forecasted values indicate that estimation of future timestamps is not supported by algorithm pSeriesNames Input Output Null terminated string of names of input time series separated by new line characters n exactly in same order as they appear on p nputMatrix On output should contain the names of output series exactly in same order as they appear in pOutputMatrix iBufLen Input The size of buffer to hold the model info pModelBuffer Output The buffer to hold the model info which can be used by algorithm next time it is invoked for updates The framework supports correct storage of this buffer in binary form And provides it exact in all consequent calls Each algorithm is free to store any info in this buffer and ask to allocate memory for it iParamLen Input The size of buffer to hold the model parameters pModelParameters Input The buffer to hold the model parameters which specify the various conditions on how to calculate forecasts This information can be interactively queried from the user through SettingsForm function int UpdateForecasts int iNumInputs int ilnputLen double plnputMatrix int iNumOutputs int iForecastLen double pOutputMatrix double pVarianceMatrix double pDateTime char pSeriesNames int BufLen void pModelBuffer int iParamLen char pModel
9. ALAMOSA PCS HLD a mas ha E The model tree has the context menu to IE ALA O ll aif ALA MltiChart expand and collapse its items and get access E ALA Fa Table to model nodes Alternatively you can double Ay ALB E Descri i click on each node to get access to the 6 10 ALB p corresponding model H ALB Delete d aA ALB td Creation of new model e ALB BE Name E ALB a To create new master model use command 6 12 acc Expand New from menu File Newly created model Ah ALC TE Collapse contains no time series To add some series fy ALC z use data import options from the same menu C or click Add Series in menu Model In the later case the system generates for you very short sample series which consists of two points You can use that series in further work by changing its name and adding additional points as needed or removing initially created points Creating the new master model you automatically close the current active model Saving model You can save current master model for the later use in a special format of Aura This format actually represents the complex high performance object storage which permanently holds your full current working state and the reloads it by demand The model is saved to the special file with the extension arm AuRaModel To save model use command Save from menu File Opening model uk All files of Aura are saved
10. Aura AutoRelation Analysis The expert system in multivariate adaptive forecasting Version 3 1 User Manual Copyright 1999 2001 Boris Zinchenko amp Econom Expert Ltd Moscow January 2002 The information contained in this guide is subject to change without notice Econom Expert Ltd shall not be liable for errors contained herein or for consequential damages in connection with the furnishing performance or use of the material No part of this document may be reproduced or transmitted in any form or by any means electronic or mechanical for any purpose or translated to another language without the prior written consent of Econom Expert Ltd All products or brand names used in this guide are trademarks or registered trademarks of their respective companies Econom Expert Ltd Teply Stan 8 41 Moscow 117133 Russia 7 095 339 28 58 econexpert mtu net ru http www geocities com aforecasts Copyright 1999 2001 Boris Zinchenko amp Econom Expert Ltd All Rights Reserved PRODUET OVERVIEW ios a a A a ai 4 MAJOR FEATURES a A e cia 4 STATISTICAL MODELING oooooccnmommmmmmmsmmmmmmm PROBLEM DESCRIPTION ni id ds 6 APPLICATION CONCEPT A A A dl 6 DATA DIMENSIONS carene a A ce 7 ALGORITHMS A A A E aE 7 SIMPLE DEFINITIONS ocsscdedesecsavesscestuvedeccnvosgnestugedvesaveeduesdededvosedsusegedusedessanvacencivecdussvugednastecednoseeseseses 9 STATISTICAL model ca ot Codd EEE A dace SENE
11. N E E E ETE E E E E EAEE 9 Input IR RNA Ouiput of Mode a tie FOFeCASUNE CISOTUN ML titi MASTER MODEL iia Model tee e NA AA SEN A he a te Ra A A e A A A a pe En Creation of new model SAVING NOEL a Ae Opening model i cots hee See ial Noe See SR SL Seah Closing model Calculation OJOTAS A Tea td TNSCTUNG NEW SCTICS a ia tenida a Mecsdeleavoddntbesisdelseuntacseesesats Deleting series TIME SERTES aaa at AUCs eee ie eh scr eRe ey seas ae seas al eter rieles Series GING vis cd eccv E eg iii rada Model parameters 0 Point addition das Removal Of observations ssccceseceeseecessecesseceeeecesnecescecesseeesseecssceseseecesseeesseeseeeseseesesaeeesaeessaeees 12 DATA REPRESENTATION irc entra a ce Isdin de veceutusecuund dove eee iii eii inci is 12 Welcome Screen iii ESR en deed catdecducelsceducetecsuadedeceacedeestadedeested 12 Cra A A 12 Tal asada ico 13 Model description A A E Eea 13 DATA EXCHANGE FORMATS nn arn h Gus devdcer shade condo siria cio eaten id ii 14 CUpboOard SUPppOrt lt a A da 14 Clip oard IMPONE Ni 14 Open financial CONNECTIVITY ceeecessceceseecessecesaccessceesseeceseecescecesaeeesaeeesaeecsseeceseesesaeeesseeesaeeseaeers 14 FORECASTING MODEL SPECIFICATION ooocccconncoononconnnconancocanoccnnconnnconcnconccnconaconacncoccconuc conos LO UNIFIED SPECIFICATION OF INTERFACES TO THE EXPERT SHELL AND ALGORITHM LIBRARY coooo 17 Globally identify algorithm in algorithm library oooonnnncnnn
12. Parameters Update forecasts based on previously calculated and stored model If not supplied no fast update option is not available for the model In that case the function CalculateForecasts is called with the same set of parameters and the information contained in pOutputindex and pModelBuffer is erased Numinputs Input Number of input series iInputLen Input The length of each input series pInputMatrix Input The array of size exactly NumInputs InputLen containing all input series in the following order First InputLen elements contain first series next InputLen second series and so on till the last iNumInputs series iNumOutputs Input Number of output series iForecastLen Input The desired length of forecast pOutputMatrix Output Matrix of dimension iNumOutputs iInputLen IforecastLen to hold forecasts and backforecasts calculated On completion it must hold output series in the following order First iInputLen IforecastLen elements must contain the first output series see the figure next iInputLen IforecastLen elements the second and so on till the last iNumOutputs series Each output series in turn must hold in 2 InputLen locations the backforecasts one step ahead corresponding to exactly the same observed points of input series or NaN values if corresponding backforecast cannot be calculated Locations iInputLen 1 iinputLen iForecastLen of the ou
13. an exchange any information with other applications through standard windows clipboard To do that just select any region of data table and click Copy from menu Edit Then go to application where data must be placed and click Paste All data will be moved In this way data can be moved to any desktop application as MS Excel To export picture use the same commands Clipboard import The same mechanism can be used to import data from other applications into Aura To do that use the following sequence e Select the region of data in source application The data must be arranged in the same order as in Aura i e in two columns with first column containing dates in standard OLE format as MS Excel The second row must contain observed values Also insertion of only one column is possible dates or values e Copy the selected data from source application to clipboard through Copy command e Go to Aura and select target series Open table form of that series Select the region in the table where the data should be placed Use command Paste from menu Edit If the destination series is shorter than the data in clipboard then the additional rows will be added to the table as necessary The observation will be automatically sorted during insertion so that their final order can differ from the original Open financial connectivity Probably the most powerful of all interfaces Aura offers in connect
14. and static table with the back forecasts The exact format depends on model level for which the table is displayed If the model belongs to upper level of model tree then the input table shows allowing for user input of series data If on the other hand subsidiary model is displayed then static table takes place which takes all data from calculated arrays The first row of each table contains observation dates All observations ALASKA AIR GRO E are automatically sorted by their dates If input is enabled then the date can be edited either directly or through the special popup dialog appearing from down button on the right of active date cell The second column contains the observed values To edit them just click on corresponding cell Input table also contains the forecasts on the bottom of this column which cannot be edited The forecasts are highlighted in different color If no forecasts are present the you must calculate the model to observe them The static table has two additional columns forecast and variance The forecast column contains the actual forecasts in its bottom as well as backforecasts calculated for certain period in the past and shown in parallel with the observed values to estimate the residuals of forecasting model The last column contains the variances of the corresponding forecasts and backforecasts The variances are estimated with the confidence level set through model setup dialog in command
15. ects the best combination of algorithms in each particular case Problem description While there are many computer programs aimed to resolve this problem they often fail to meet expectations of a novice user at least in the following ways e Tightly specialized applications can effectively forecast only a narrow set of typical situations The nuisance is situations tend to change swiftly e Diverse statistical analyzers require at least a moderate knowledge of the algorithms they expose Such knowledge costs time and money e Powerful and flexible neuronets appear quite dumb on a short data series In fact additional data happen to be expensive if available at all To consolidate and mutually reinforce the above approaches we suggest the universal shell built up as an expert system and aimed to combine the most popular forecasting algorithms in the automated competitive environment Given a multidimensional time series the system automatically hypothesizes on a set of all available solutions seeking to minimize the aggregate difference between backforecasts and the actual values of the supplied series The most effective hypotheses then integrate into the final model which in turn is used to predict future responses Application concept Aura implements the fractal computational network based on the stochastic propagators The individual knots of this network may be any forecasting algorithms The adjective fractal does
16. elete the current series from the model use command Delete Series from menu Model This command deletes the series which is currently displayed in the active window If no such window is present then the series highlighted in the model tree is removed You will be warned before deletion Deleted series cannot be restored Time series Generally time series is the sequence of observations ordered according to their dates Aura does not require that observations be separated with equal periods of time In this way the actual observation time becomes the essential part of the model It drastically distinguishes Aura from other statistical packages where only the order of observations plays role Aura displays the time series and its model as one entity in the model tree So you cannot generally distinguish between them Series name Each series in Aura must have the unique name That name unifies all models that descend from this series in one model cluster which expands from root nodes in the model tree You can change series name any time by simply editing it in table head or by command Name from menu Series In this way you simultaneously rename all models corresponding to this series Model parameters The overall model parameters for each series can be set individually through command Tune from menu Model This command displays the special dialog where you can specify forecast length and conf
17. h forecasting model consists of three main parts input output and forecasting algorithm Input of the forecasting model is the collection of time series Output of the model is the collection of forecasted time series with probability limits and backforecasts for the model verification Forecasting algorithm is any function transforming input to output hopefully with the good forecasting accuracy Forecasting I algorithm where number of inputs k number of outputs n the length of each input series m the length of each forecast Each input is the numeric array of length n Each output consists of two numeric arrays say ArrayForecast and ArrayVariance of length n m each ArrayForecast in its first n positions contains backforecasts at exactly the same time lags as the observed points of the input time series This information must be produced by forecasting algorithm for model verification If some of the backforecasts cannot be calculated then they must be filled with NaN values The last m positions of ArrayForecast must contain forecasts of one to msteps ahead ArrayVariance must contain the variances of the forecasts present in the ArrayForecast exactly in the same order or NaN if the corresponding variance cannot be obtained Corresponding input and output data structures can be implemented in any programming language as well as a few additional functions for algorithm control and model storage The
18. idence level in modeling each series All other model parameters are detected automatically by the expert system during calculations Point addition To add new observations to the series use command New Observation from menu Series or corresponding button in the toolbar New observation is always inserted in the end of time series and assigned the correct next date depending on the frequency of previous observations If you wish to insert point in different place just edit its date after insertion and new point will be automatically relocated to correct place The default value of new observation is automatically set to zero New value can be printed in place as necessary Removal of observations To remove the observation just select it in table and click Delete Observation from menu Series If no observation is currently selected this command is not available Also be aware that each time series must contain at least one observation Data representation To make work most effective Aura offers several formats in which original data and forecasts can be represented Those include charts tables and hypertext reports You can always choose one that best fits your current needs If you get lost in many windows select Main Model in menu View or just click home button on toolbar to return to initial screen All forms for each series are collected in one tabbed window for rapid access You ca
19. in special files with the extension arm AuRaModel To open such file from Windows Explorer just double click on its icon To do the same form Aura shell use command Open from menu File or alternatively use the list of recently opened models downside of the same menu Closing model To close current master model use command Close from menu File Closing the model removes all its views from the screen including Structure of model window Calculation of forecasts To calculate forecast just click Calculate in menu Model Then the system automatically searches for the best model and calculates forecasts using that model After the calculations are finished all charts and tables are properly updated Calculation of whole model may be very time consuming process If you have already done it and then did minor changes to input data as say added a couple of points to lengthy time series you don t have to recalculate the whole model You can use command Update in the same menu to just update forecasts using the same model It s much faster and don t affect the accuracy Inserting new series To insert new series into the model use command New Series from menu Model Simple series with two points is then inserted New series are automatically enumerated consequently as Series 1 2 you can change series name and fill it with your data Deleting series To d
20. ing to financial data This functionality is accessed through command Import from menu File This command displays the special dialog which contains the list of financial data servers installed on your computer To select the right server just double click on its icon or select it and push Next Then program automatically connects to the server and displays all databases present on it You can see the list of databases on the screen that appears Select the right database from the list open and even create additional databases if corresponding driver supports these operations When you decided which database to open just double click on its icon The program automatically connects to the selected database and displays all its data tables in the left pane of a special dialog Use the mouse and special buttons in the middle of the dialog to select tables and move them to the right list When done click OK and the process of data import will start The progress dialog will inform you on which data are currently imported Cancel button permits stopping that process any time Aura supports rich choice of standard servers which fully conform to the latest standards of financial data providers Moreover Aura offers exceptionally simple fast and reliable open standard for development of additional drivers so that independent developers can supply any drivers of their choice Forecasting model specification Eac
21. n Returns the buffer allocation size requested from algorithm to hold its parameters If not implemented the standard 64 kb is allocated by default iNumInputs Input Number of input series iInputLen Input The length of each input series iNumOutputs Input Number of output series iForecastLen Input The desired length of forecast int MaxReportLen int iNumInputs int iInputLen int iNumOutputs int iForecastLen Returns the buffer allocation size requested from algorithm to hold its report If not implemented the standard 64 kb is allocated by default iNumInputs Input Number of input series 20 iInputLen Input The length of each input series NumOutputs Input Number of output series ForecastLen Input The desired length of forecast int MaxSettingsLen int iNumInputs int iInputLen int iNumOutputs int iForecastLen Returns the buffer allocation size requested from algorithm to hold its model settings web form If not implemented the standard 64 kb is allocated by default iNumInputs Input Number of input series iInputLen Input The length of each input series iNumOutputs Input Number of output series iForecastLen Input The desired length of forecast Functions to tell algorithm capabilities to the framework int MinInputLen Returns the minimum length of input Default is 1 int MaxInputLen Returns the maximum length of input Default is MAX_INT int MinForecastLen
22. n click corresponding tab or use menu View to select right form Clicking in model tree makes the same job Below are listed available forms Input data ALCIDE CORP a Chart 0 Displays comparative tendencies ofthe observed series and forecasts in a concise analytic presentation Ex Table E Gives the parallel ypresentation of the time series forecasts and their variance estimates in a compact and unified form gt Description SS Reveals insight into exact details and mathematical structure SG of the model which was used for the series prediction Relayed by Aura Forecast Engine Copyright Econom Expert Ltd 1999 2001 Graph Welcome screen That is original hypertext screen available for each model and offering navigation to all other screens available To navigate click hyperlinks on that page as on any other web page The list of other screens depends on exact model Most models offer chart table and model description screens This screen displays the original input series backforecasts for the series future forecasts and confidence intervals for forecasts and ALASKA AIR GROUP backforecasts in different colors Colors and signs for each graph component are displayed below Series name is shown above the graph Both series name and legend can be hidden to extend display through submenu Chart in menu Format Table Data tables can have two different formats data input table
23. ncnnnanononncnnnnnnnnn conan conan no nnn coran crnnn nro 17 Forecasting core LUNCHONS LA aa ea aa eani 17 Functions to estimate buffers needed cccccescccessecessecessecesseecesceceseecesseeesaeesseessseecesaeeeseessaeees 20 Functions to tell algorithm capabilities to the framewoTKk oooonnnncnnnncnnnncnnnacnnonncnonnnrnnncrnnanonncoo 21 Introduction Product AEE 181 x E Eile Edit View Format Series Model Window Help Welcome to the Aura User s Guide This guide provides an introduction to Aura and gives you all the information you need to work with the product The guide is intended to help new users of Aura It provides basic information applicable across the core technology and explains concepts necessary to start working Visit our site http www geocities com aforecasts to learn more about Aura and to download the latest demo version of this package Overview Aura s the automated expert system for multivariate statistical forecasting lt combines the unique power of full automated multivariate statistical analysis in unlimited dimensions with the remarkable ease of use Be you the experienced mathematician or just a novice in forecasting with immediate and very practical goals Aura is just for you It can both offer the instant forecast by one mouse click or expand for you many levels of complicated model trees that stay behind a few final digits You can watch accurate and lazes gt cu Sy
24. not mean that uses Aura fractal algorithms for forecasting purposes although it is fairly possible It points in current context that Aura itself is fractal In fact fractal is defined as the selfsimilar structure produced by a set of invariant derivation rules Aura network is exactly such structure It can hold infinite number of modeling levels Each modeling level can hold the arbitrary amount of forecasting models Each model in turn obeys the derivation rules of the homomorphic hierarchy of interfaces and can hold itself infinite layers of such interfaces in itself Awra as such derives exactly from such interface so it can hold itself in recursion to infinite order Individual forecasting models with the unified interfaces may be interpreted as the knots of the Aura fractal network To connect them into the optimal forecasting strategy Aura uses the concept of stochastic propagators Each propagator represents the series of stochastic points with the certain variance estimated for each point The network uses a number of optimization techniques to ensure the serial convergence of propagators to the minimum level of variance In this way the optimal forecast is achieved In contrast to the most other statistical packages Aura operates not on traditional time series It operates on individual observation points So it can combine in a single model the data with very different time steps from fraction of seconds to many yea
25. pable to combine various forecasting algorithms Multivariate statistical models of arbitrary dimensions in infinite level layering network Intuitive user interface with multiscale tables and charts featuring confidence estimates and residuals Transparent model structure with multilevel hypertext navigation delivered through built in web server Own standard of superfast unlimited object storage with multi user support Unattended automated operation with the profiling logs and smart error recovery Open financial connectivity to all major data vendors through open standard data drivers Unlimited extensibility through unified language neutral model component standard Statistical Modeling This chapter discusses the different statistical modeling concepts of and shows how they are supported by Aura Aura is a dedicated modeling toolkit for building multivariate statistical models of very big dimensions and apply them to the live streams of the real time data for fast prediction and fast decision making Aura is not just another forecasting algorithm It implements computational matanet The individual knots of this network may be any forecasting algorithm Moreover each knot may hold the whole selfsimilar network however elaborate and complex In this way our project suggests the unique way to integrate all the imaginable forecasting algorithms into the unified self organized Al environment which automatically sel
26. rs The user should be very carefully with input observation dates for each series They must exactly coincide or else algorithm will synchronize them on its own Data dimensions With its breakthrough data analysis technology Aura is able to deal effectively with data sets of practically any length and dimensions The exact characteristics are listed in the following table Parameter Minimum Maximum Number of input data series input dimension 4 of the model not restricted The length of the individual data series Different data series within the model may 2 have different lengths We recommend at least 10 points in a series not restricted The length of forecast we recommend the 4 values not exceeding 5 not restricted Algorithms The set of algorithms currently implemented and available in the package are listed below Moreover each knot may hold the whole selfsimilar fractal network however elaborate and complex At present the project supports the following Input control procedures for automatic conversion of raw data into a data series e Monitoring and smart filtering of extreme values within data series e Mutual synchronization and aggregation of datasets e Multidimensional analysis of distributed lags e Simple linear regression for instant prediction of a very short time series Anumber of statistical tests which detect seasonal components and estimate their characteristic times e
27. sts and UpdateForecasts functions If not supplied settings changes are not supported for the model iFormLen Input The size of buffer to hold the form pFormBuffer Output The buffer where the function must store the form Html format is expected BufLen Input The size of buffer to hold the model info pModelBuffer Input The buffer which holds the model info previously stored during model calculation iParamLen Input The size of buffer to hold the model parameters pModelParameters Input The model settings parameters PathLen Input The size of buffer to hold the resource path pResPath Input The url path to the web server which processes the current model Developers can upload their own graphics etc to store on the server and use in their custom reporting pServerPath Input The url path to the web server which will processes the form Functions to estimate buffers needed int MaxBufferLen int iNumInputs int iInputLen int iNumOutputs int iForecastLen Returns the buffer allocation size requested from algorithm to hold its model info If not implemented the standard 64 kb is allocated by default Numinputs Input Number of input series iInputLen Input The length of each input series iNumOutputs Input Number of output series iForecastLen Input The desired length of forecast int MaxParamLen int iNumInputs int ilnputLen int iNumOutputs int iForecastLe
28. t InputLen second series and so on till the last iNumInputs series NumOutputs Input Number of output series ForecastLen Input The desired length of forecast pOutputMatrix Output Matrix of dimension NumOutputs iInputLen IforecastLen to hold forecasts and backforecasts calculated On completion it must hold output series in the following order First iInputLen IforecastLen elements must contain the first output series see the figure next iInputLen IforecastLen elements the second and so on till the last iNumOutputs series Each output series in turn must hold in 2 InputLen locations the backforecasts one step ahead corresponding to exactly the same observed points of input series or NaN values if corresponding backforecast cannot be calculated Locations iInputLen 1 InputLen iForecastLen of the output series must contain calculated forecasts 1 ForecastLen steps ahead pVarianceMatrix Output Matrix of dimension NumOutputs iInputLen IforecastLen to hold the variances of forecasts and backforecasts calculated exactly in the same order as pOutputMatrix pDate Time Input Output The array of size iInputLen iForecastLen which on input contains the timestamps of each observation in its first iInputLen locations On output the array should contain expected timestamps of the forecsted values in its last ForecastLen locations and also may contain NaN
29. tput series must contain calculated forecasts 1 ForecastLen steps ahead pVarianceMatrix Output Matrix of dimension iNumOutputs iInputLen lforecastLen to hold the variances of forecasts and backforecasts calculated exactly in the same order as pOutputMatrix pDate Time Input Output The array of size iInputLen iForecastLen which on input contains the timestamps of each observation in its first iInputLen locations On output the array should contain expected timestamps of the forecsted values in its last ForecastLen locations and also may contain NaN values for timestamps of input observations to indicate that corresponding points were missed by algorithm NaN timestamps of forecasted values indicate that estimation of future timestamps is not supported by algorithm pSeriesNames Input Output Null terminated string of names of input time series separated by new line characters An exactly in same order as they appear on p nputMatrix On output should contain the names of output series exactly in same order as they appear in pOutputMatrix iBufLen Input The size of buffer to hold the model info pModelBuffer Input The buffer which holds the model info previously stored during model calculation iParamLen Input The size of buffer to hold the model parameters pModelParameters Input The buffer to hold the model parameters which specify the various conditions on how to calculate forecasts
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