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Fraclet Predictor Overview 
Presented by 
Quant Trade Technologies, Inc.
2 
Contents 
 
Introduction/Motivation 
 
Survey and Lag Plots 
 
Exact Problem Formulation 
 
Proposed Method 
› 
Fra...
3 
General Problem Definition 
Given a time series {xt}, predict its future course, that is, xt+1, xt+2, ... 
Time 
Value ...
4 
Motivation 
• 
Financial data analysis 
• 
Physiological data, elderly care 
• 
Weather, environmental studies 
Traditi...
5 
Traditional Forecasting Methods 
 
ARIMA but linearity assumption 
 
Neural Networks but large number of parameters...
6 
Lag Plots 
xt-1 
xt 
4-NN 
New Point 
Interpolate these… 
To get the final prediction 
Q0: Interpolation Method 
Q1: La...
7 
Q0: Interpolation 
Using SVD (state of the art) 
Xt-1 
xt
8 
Why Lag Plots? 
› 
Based on the “Takens’ Theorem” [Takens/1981] 
› 
which says that delay vectors can be used for predi...
9 
Inside Theory 
Example: Lotka-Volterra equations 
ΔH/Δt = rH – aH*P ΔP/Δt = bH*P – mP 
H is density of prey P is densit...
10 
Problem at hand 
 
Given {x1 , x2 , …, xN } 
 
Automatically set parameters - L(opt) (from Q1) - k(opt) (from Q2) 
...
11 
Transform Data 
0.10.20.30.40.50.60.70.80.910.10.20.30.40.50.60.70.80.91 x(t) x(t-1) Logistic Parabola 
X(t-1) 
X(t) 
...
CIKM 2002Your logo here 12 
Cube the Data 
0.10.20.30.40.50.60.70.80.910.10.20.30.40.50.60.70.80.91 x(t) x(t-1) Logistic P...
13 
How Much Data is Enough? 
 
To find L(opt): 
› 
Go further back in time (ie., consider Xt-2 , Xt-3 and so on) 
› 
Til...
14 
Fractal Dimensions 
 
FD = intrinsic dimensionality 
“Embedding” dimensionality = 3 
Intrinsic dimensionality = 1
15 
Fractal Dimensions 
FD = intrinsic dimensionality [Belussi/1995] 
00.10.20.30.40.50.60.70.80.9100.20.40.60.811.21.41.6...
16 
Q1: Finding L(opt) 
 
Use Fractal Dimensions to find the optimal lag length L(opt) 
Lag (L) 
Fractal Dimension 
epsil...
17 
Q2: Finding k(opt) 
 
To find k(opt) 
• 
Conjecture: k(opt) ~ O(f) 
We choose k(opt) = 2*f + 1
18 
Logistic Parabola 
00.511.522.5312345 Fractal Dimension LagFD vs LOur Choice 
• 
FD vs L plot flattens out 
• 
L(opt) ...
19 
Prediction 
Timesteps 
Value 
Our Prediction from here
20 
Logistic Parabola 
Timesteps 
Value 
Comparison of prediction to correct values
21 
Logistic Parabola 
00.511.522.5312345 Fractal Dimension LagFD vs LOur Choice00.050.10.150.20.250.30.35123456 NMSE LagN...
22 
Lorenz Attractor 
00.511.522.5312345678910 Fractal Dimension LagFD vs LOur Choice 
• 
L(opt) = 5 
Timesteps 
Value 
La...
23 
Lorenz Attractor Prediction 
Value 
Timesteps 
Our Prediction from here
24 
Prediction Test 
Timesteps 
Value 
Comparison of prediction to correct values
25 
Optimal Prediction 
00.511.522.5312345678910 Fractal Dimension LagFD vs LOur Choice 
L(opt) = 5 
Also NMSE is optimal ...
26 
Laser 
00.511.522.533.51234567891011121314151617 Fractal Dimension LagFD vs LOur Choice 
• 
L(opt) = 7 
Timesteps 
Val...
27 
Prediction 
Timesteps 
Value 
Our Prediction starts here
28 
Prediction Test 
Timesteps 
Value 
Comparison of prediction to correct values
29 
Optimal Prediction 
00.511.522.533.51234567891011121314151617 Fractal Dimension LagFD vs LOur Choice00.511.522.533.512...
30 
Speed and Scalability 
 
Preprocessing is linear in N 
 
Proportional to time taken to calculate FD 
500100015002000...
31 
The Fraclet Way 
Our Method: 
 
Automatically set parameters 
 
L(opt) (answers Q1) 
 
k(opt) (answers Q2) 
 
In l...
32 
Conclusions 
 
Black-box non-linear time series forecasting 
 
Fractal Dimensions give a fast, automated method to s...
33 
Pioneers in the fractal exploration of financial markets 
Trading futures and options involves the risk of loss. You s...
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Fractal Forecasting of Financial Markets with Fraclet Algorithm

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Fractal Forecasting of Financial Markets with Fraclet Algorithm

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Fractal Forecasting of Financial Markets with Fraclet Algorithm

  1. 1. Fraclet Predictor Overview Presented by Quant Trade Technologies, Inc.
  2. 2. 2 Contents  Introduction/Motivation  Survey and Lag Plots  Exact Problem Formulation  Proposed Method › Fractal Dimensions Background › Our method  Results  Conclusions
  3. 3. 3 General Problem Definition Given a time series {xt}, predict its future course, that is, xt+1, xt+2, ... Time Value ?
  4. 4. 4 Motivation • Financial data analysis • Physiological data, elderly care • Weather, environmental studies Traditional fields Sensor Networks (MEMS, “SmartDust”) • Long / “infinite” series • No human intervention “black box”
  5. 5. 5 Traditional Forecasting Methods  ARIMA but linearity assumption  Neural Networks but large number of parameters and long training times  Hidden Markov Models O(N2) in number of nodes N; also fixing N is a problem  Lag Plots
  6. 6. 6 Lag Plots xt-1 xt 4-NN New Point Interpolate these… To get the final prediction Q0: Interpolation Method Q1: Lag = ? Q2: K = ?
  7. 7. 7 Q0: Interpolation Using SVD (state of the art) Xt-1 xt
  8. 8. 8 Why Lag Plots? › Based on the “Takens’ Theorem” [Takens/1981] › which says that delay vectors can be used for predictive purposes
  9. 9. 9 Inside Theory Example: Lotka-Volterra equations ΔH/Δt = rH – aH*P ΔP/Δt = bH*P – mP H is density of prey P is density of predators Suppose only H(t) is observed. Internal state is (H,P).
  10. 10. 10 Problem at hand  Given {x1 , x2 , …, xN }  Automatically set parameters - L(opt) (from Q1) - k(opt) (from Q2)  in Linear time on N  to minimise Normalized Mean Squared Error (NMSE) of forecasting
  11. 11. 11 Transform Data 0.10.20.30.40.50.60.70.80.910.10.20.30.40.50.60.70.80.91 x(t) x(t-1) Logistic Parabola X(t-1) X(t) The Logistic Parabola xt = axt-1(1-xt-1) + noise time x(t) Intrinsic Dimensionality ≈ Degrees of Freedom ≈ Information about Xt given Xt-1
  12. 12. CIKM 2002Your logo here 12 Cube the Data 0.10.20.30.40.50.60.70.80.910.10.20.30.40.50.60.70.80.91 x(t) x(t-1) Logistic Parabola x(t-1) x(t) x(t-2) x(t) x(t) x(t-2) x(t-2) x(t-1) x(t-1) x(t-1) x(t)
  13. 13. 13 How Much Data is Enough?  To find L(opt): › Go further back in time (ie., consider Xt-2 , Xt-3 and so on) › Till there is no more information gained about Xt
  14. 14. 14 Fractal Dimensions  FD = intrinsic dimensionality “Embedding” dimensionality = 3 Intrinsic dimensionality = 1
  15. 15. 15 Fractal Dimensions FD = intrinsic dimensionality [Belussi/1995] 00.10.20.30.40.50.60.70.80.9100.20.40.60.811.21.41.61.82 Y axis X axisSierpinsky78910111213141516-7-6-5-4-3-2-1012log(# pairs within r) log(r) FD plot= 1.56 log(r) log( # pairs) Points to note: • FD can be a non-integer • There are fast methods to compute it
  16. 16. 16 Q1: Finding L(opt)  Use Fractal Dimensions to find the optimal lag length L(opt) Lag (L) Fractal Dimension epsilon L(opt) f
  17. 17. 17 Q2: Finding k(opt)  To find k(opt) • Conjecture: k(opt) ~ O(f) We choose k(opt) = 2*f + 1
  18. 18. 18 Logistic Parabola 00.511.522.5312345 Fractal Dimension LagFD vs LOur Choice • FD vs L plot flattens out • L(opt) = 1 Timesteps Value Lag FD
  19. 19. 19 Prediction Timesteps Value Our Prediction from here
  20. 20. 20 Logistic Parabola Timesteps Value Comparison of prediction to correct values
  21. 21. 21 Logistic Parabola 00.511.522.5312345 Fractal Dimension LagFD vs LOur Choice00.050.10.150.20.250.30.35123456 NMSE LagNMSE vs LagOur Choice Our L(opt) = 1, which exactly minimizes NMSE Lag NMSE FD
  22. 22. 22 Lorenz Attractor 00.511.522.5312345678910 Fractal Dimension LagFD vs LOur Choice • L(opt) = 5 Timesteps Value Lag FD
  23. 23. 23 Lorenz Attractor Prediction Value Timesteps Our Prediction from here
  24. 24. 24 Prediction Test Timesteps Value Comparison of prediction to correct values
  25. 25. 25 Optimal Prediction 00.511.522.5312345678910 Fractal Dimension LagFD vs LOur Choice L(opt) = 5 Also NMSE is optimal at Lag = 5 00.20.40.60.81024681012 NMSE LagNMSE vs LagOur Choice Lag NMSE FD
  26. 26. 26 Laser 00.511.522.533.51234567891011121314151617 Fractal Dimension LagFD vs LOur Choice • L(opt) = 7 Timesteps Value Lag FD
  27. 27. 27 Prediction Timesteps Value Our Prediction starts here
  28. 28. 28 Prediction Test Timesteps Value Comparison of prediction to correct values
  29. 29. 29 Optimal Prediction 00.511.522.533.51234567891011121314151617 Fractal Dimension LagFD vs LOur Choice00.511.522.533.512345678910111213 NMSE LagNMSE vs LOur Choice L(opt) = 7 Corresponding NMSE is close to optimal Lag NMSE FD
  30. 30. 30 Speed and Scalability  Preprocessing is linear in N  Proportional to time taken to calculate FD 5001000150020002500300035004000450050002000400060008000100001200014000160001800020000 Preprocessing Time Number of points (N) Time vs N
  31. 31. 31 The Fraclet Way Our Method:  Automatically set parameters  L(opt) (answers Q1)  k(opt) (answers Q2)  In linear time on N
  32. 32. 32 Conclusions  Black-box non-linear time series forecasting  Fractal Dimensions give a fast, automated method to set all parameters  So, given any time series, we can automatically build a prediction system  Useful in a sensor network setting
  33. 33. 33 Pioneers in the fractal exploration of financial markets Trading futures and options involves the risk of loss. You should consider carefully whether futures or options are appropriate to your financial situation. You must review the customer account agreement and risk disclosure prior to establishing an account. Only risk capital should be used when trading futures or options. Investors could lose more than their initial investment. Past results are not necessarily indicative of futures results. The risk of loss in trading futures or options can be substantial, carefully consider the inherent risks of such an investment in light of your financial condition. Information contained, viewed, sent or attached is considered a solicitation for business. Quant Trade, LLC has been a Commodity Futures Trading Commission (CFTC) registered Commodity Trading Advisor (CTA) since September 4, 2007 and a member of the National Futures Association (NFA). Copyright @ 2012 Quant Trade, LLC. All rights reserved. No part of the materials including graphics or logos, available in this Web site may be copied, reproduced, translated or reduced to any electronic medium or machine- readable form, in whole or in part without written permission. 2 N Riverside Plaza Suite 2325 Chicago, Illinois 60606 Quant Trade LLC (872) 225-2110

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