Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q10/00—Administration; Management
  • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
  • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Definitions

• the present invention relates to a computer system for assisting with prediction.
• Such a system can be considered as having a topological structure relating to a chronological set of numerical values making it possible to predict how said game will later be enriched with new values.
• simple computer systems can not predict reliably the future of a chronological game of values.
• the system proposed here makes it possible to predict, using inexpensive computer means, the future of a chronological game of values with a high degree of confidence.
• the system includes a computer, a display screen, or other display medium, and allows the following steps to be performed as detailed below:
• the system also includes a procedure for assisting the fictitious extension prediction of the chronological set of values.
• Figure 1 schematically illustrates, in the form of a diagram, the structure of the system.
• O, J, H, A, V, U, PA and P respectively denote the computer, the chronological set of numerical values, the memory, the analyzer, the visu, the user, the procedure for assisting the prediction and forecasting.
• FIG. 2 represents the construction of four curves C1, C2, C3 and C4 based on linear regressions from the values of the game represented in the form of the curve C.
• FIG. 3 represents a network of curves based on linear regressions.
• Figure 4 shows a prolonged topological structure through the prediction help procedure.
• the chronological set of values J is loaded in the central memory H of a computer O or on one of its storage units from a storage medium, for example a cd-rom, or through a transmission in the form of a stream.
• the numerical values of the chronological game are used by said algorithm to produce a dense network of curves constituting the topological structure of said game.
• the algorithm defined below uses the known mathematical tool that is the degree (or order) regressions D.
• the algorithm can use any of the following degree D regressions:
• - Xo is the last (most recent) abscissa of the chronological game of values; M is the number of values of the chronological game up to abscissa Xo;
• N is a parameter representing a desired number of curves in the desired curve network
• ni is the first term, of the set ⁇ n lv .., ni c , ..., n ⁇ , that is to say the number of values used for the regressions making it possible to construct the curve Ci of network;
• nN is the last term of the set ⁇ m, ..., nk, ..., n ⁇ , that is to say the number of values used for the regressions making it possible to construct the curve C N of network final moments of calculation;
• the algebraic formula [1] is used to calculate ni o whose rounding represents the number of consecutive values ("main parameter") used by each linear regression of rank k.
• ni, II N and a are chosen in advance on the basis of the Q criteria detailed below.
• the algorithm N 0 I decomposes into a regression calculator (calculation of each regression and optionally generation of segments) and a controller which determines these thanks to the algebraic formula [I].
• the algorithm N ° 1 provides a network of curves (discontinuous or continuous if we chooses the option appearing in the loop 2) forming a topological structure in which can appear characteristic figures, useful for the forecast.
• the curves can be visualized using different colors.
• the abscissae numbered 1, 2, 3, 4 and 5 respectively represent the abscissae x-M + p (i), x-M + p (2), X-M + P (3), X-M + P (4), first points of each curve (C1, C2, C3, C4), and the abscissa of the first value X- M + I of the set of values.
• a chord is a pronounced condensation of curves that detaches from a less dense background of network curves.
• An envelope draws the limit of a group of curves of the network.
• a hem is both a rope and an envelope.
• the network of curves must be dense, that is to say that the number N of curves of the network is sufficiently large. In practice, this number must be greater than about 20. In order to bring out the characteristic figures, this number must, ideally, be greater than 100.
• the set of values n k of the main parameter must extend over a beach
• the third criterion is satisfied when the values of the set grow slowly and uniformly.
• one can slightly play on the density for example to densify the network in the area of small values of the main parameter.
• the algebraic formula used in the algorithm No. 1 makes it possible to determine the values of the set with a more than sufficient precision, including in the case where it is desired to play on the density. Its condition of validity is:
• the algorithm can be alleviated by using a predefined set of main parameter values, for example ⁇ 6, 18, 30, 42, 55, ..., 2415, 2436, 2457, 2479, 2500 ⁇ .
• the algorithm N 0 I can be formulated differently provided that it leads to the construction of the network of N curves relating to the chronological set of M values.
• the iteration in the loop 2 can be done in the increasing direction of the abscissa indices of the values of the chronological game. In this case, we can do without algorithm # 2, described later.
• FIG. 3 which represents a network of curves based on linear regressions, we see characteristic figures comprising strings Ia, Ib, Ic, Id, envelopes 2a, 2b, 2c, hems 3a, 3b and the curve representative of values of the game 4 represented in the form of a continuous curve.
• the prediction of the fate of the chronological game is based on the examination of a set defined on a rather broad abscissa segment comprising ropes, envelopes, hems and the representative curve of the chronological game of values. It suffices that the corresponding part of the topological structure contains in the upper part of the network a peripheral characteristic figure presenting a maximum and at the bottom of the network a peripheral feature pattern having a minimum for the segment to be considered large enough.
• the network comprises in the upper part a peripheral characteristic figure having a maximum 5 and in the lower part of the network a peripheral characteristic figure having a minimum 6.
• the aim of the examination is to determine, by analogy with past topological structures, what is the "attractive-repulsive" effect at abscissa X 0 , without crossing the strings 7a, 7d, 7e, 7i, with crossing of the strings 7b, 7c, 7f, 7g of the characteristic figures of said set on the representative curve of the chronological set of values.
• a characteristic figure will attract-push the representative curve of said values according to its type, shape and position relative to the aforementioned representative curve.
• the representative curve 4 is successively attracted-repulsed by:
• the algorithm No. 2 involves, as in the algorithm No. 1, the regression calculator and the controller.
• the procedure for fictitiously extending the time set of values can include the following steps: 1) Add to the time set of values dummy values v u , v v , ..., v y , v z , d abscissas x u , Xv, - xy, xy, x z , representing the stages of a plausible becoming of the set of values; 2) Construction of a new chronological set of values by adding the points obtained by linear interpolation between the points (v 0 , X 0 ) and (v u , x u ), (v u , x u ) and (v y , Xy) 5 ..., (v Y , Xy) and (v z , x z ); 3) Application of the algorithm N ° 2 to prolong the curves of the network since the abscissa
• a fictitious value (v, x) can be done by pointing, for example using a mouse, directly on the image of the network at the corresponding position.
• Figure 4 described below given by way of example, provides a better understanding of the procedure for assisting the forecast.
• FIG. 4 represents a valid case of prolonged topological structure obtained from 3 U, V, Fictive values.
• 4u, 4v and 4w represent a plausible evolution of the representative curve of the value set from abscissa X 0 .
• the value U, chosen to produce a valid topological extension is such that the rope 1 exerts an attraction without crossing on the fictitious curve 4u.
• the value V, chosen to produce a valid topological extension is such that the envelope 2 exerts a non-traversing attraction on the fictitious curve 4v.
• the value W, chosen to produce a valid topological extension is such that the rope 1 exerts an attraction with crossing and the hem 3 an attraction (not specified) on the fictitious curve 4w.
• each value v c of the chronological collection is assigned a weight ⁇ c , it can be passed on to the network.
• the system uses the Internet, a data server, a microcomputer computer equipped with a display screen.
• the steps detailed in the description are carried out in the form of software.
• the software includes a data processing part and a graphic display part.
• the regressions are of degree greater than or equal to 1; - the values (pi, p 2 , ..., P N ) are the integer roundings of the numbers obtained thanks to the algebraic formula [1];
• N is greater than 100; several colors are used for displaying the curves of the network and of the curve representative of the set of values;
• a fictitious value can be done by pointing with a mouse directly on the image of the network at the corresponding position.
• the invention can be applied to prediction in many technical fields. It is particularly suitable for phenomena with relatively high inertia, accompanied by a fairly strong tendency to chaotic behavior. This is the case, without limitation, in the following areas: meteorology, economy, financial markets, seismology, population dynamics, but one could also apply the invention in the fields of political science, sociology.
• the forecast includes curve analysis, which is visual here, but could be made automatic.

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• 2005
  • 2005-07-12 US US11/178,533 patent/US20070016507A1/en not_active Abandoned
• 2006
  • 2006-07-07 CN CN200680031109.5A patent/CN101248448B/zh not_active Expired - Fee Related
  • 2006-07-07 JP JP2008520913A patent/JP5101500B2/ja not_active Expired - Fee Related
  • 2006-07-07 US US11/988,624 patent/US8301675B2/en active Active
  • 2006-07-07 WO PCT/FR2006/001639 patent/WO2007006943A2/fr active Application Filing
  • 2006-07-07 EP EP06778814A patent/EP1902412A2/de not_active Ceased

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