Price Pattern Prediction Bibliography #1
Chaos, Wavelets, Fractals, Statistics, ANN's, GA's, Pattern Recognition and Time Series Prediction
Price Pattern Prediction Bibliography #2
Economics, Finance, and the Stock Market
Price Pattern Prediction Bibliography #3
Journals and Periodicals regarding Time Series Analysis, Technical Analysis of Capital Markets, Pattern Recognition, and Artificial Intelligence.
Time Series Analysis and Forecasting Techniques
A Summary of Forecasting Methods
Introduction
Widely used especially for short to intermediate term analysis-forecasting the value of items affected by factors other than time-simple regression when only one explanatory factor considered-can be done on a hand calculator.
Multiple Regression Analysis: Used when two or more independent factors are involved-widely used for intermediate term forecasting. Used to assess which factors to include and which to exclude. Can be used to develop alternate models with different factors.
Nonlinear Regression: Does not assume a linear relationship between variables-frequently used when time is the independent variable.
Trend Analysis: Uses linear and nonlinear regression with time as the explanatory variable-used where pattern over time.
Decomposition Analysis: Used to identify several patterns that appear simultaneously in a time series-time consuming each time it is used-also used to deseasonalize a series
Moving Average Analysis: Simple Moving Averages-forecasts future values based on a weighted average of past values-easy to update.
Weighted Moving Averages: Very powerful and economical. They are widely used where repeated forecasts required-uses methods like sum-of-the-digits and trend adjustment methods.
Adaptive Filtering A type of moving average which includes a method of learning from past errors-can respond to changes in the relative importance of trend, seasonal, and random factors.
Exponential Smoothing: A moving average form of time series forecasting-efficient to use with seasonal patterns- easy to adjust for past errors-easy to prepare follow-on forecasts-ideal for situations where many forecasts must be prepared-several different forms are used depending on presence of trend or cyclical variations.
Hodrick-Prescott Filter: This is a smoothing mechanism used to obtain a long term trend component in a time series. It is a way to decompose a given series into stationary and nonstationary components in such a way that there sum of squares of the series from the nonstationary component is minimum with a penalty on changes to the derivatives of the nonstationary component.
Modeling and Simulation: Model describes situation through series of equations-allows testing of impact of changes in various factors-substantially more time-consuming to construct-generally requires user programming or purchase of packages such as SIMSCRIPT. Can be very powerful in developing and testing strategies otherwise non-evident.
Certainty models give only most likely outcome-advanced spreadsheets can be utilized to do "what if" analysis-often done e.g.; with computer-based spreadsheets.
Probabilistic Models Use Monte Carlo simulation techniques to deal with uncertainty-gives a range of possible outcomes for each set of events.
Forecasting error: All forecasting models have either an implicit or explicit error structure, where error is defined as the difference between the model prediction and the "true" value. Additionally, many data snooping methodologies within the field of statistics need to be applied to data supplied to a forecasting model. Also, diagnostic checking, as defined within the field of statistics, is required for any model which uses data.
Using any method for forecasting one must use a performance measure to assess the quality of the method. Mean Absolute Deviation (MAD), and Variance are the most useful measures. However, MAD doesn't lend itself to further use making inferences, but that the standard error does. For the error analysis purposes variance is preferred since variances of independent (uncorrelated) errors are additive. MAD is not additive.
An Introduction to Wavelets
Abstract
Wavelets are mathematical functions that cut up data into different frequency components, and then study each component with a resolution matched to its scale. They have advantages over traditional Fourier methods in analyzing physical situations where the signal contains discontinuities and sharp spikes. Wavelets were developed independently in the fields of mathematics, quantum physics, electrical engineering, and seismic geology. Interchanges between these fields during the last ten years have led to many new wavelet applications such as image compression, turbulence, human vision, radar, and earthquake prediction. This paper introduces wavelets to the interested technical person outside of the digital signal processing field. I describe the history of wavelets beginning with Fourier, compare wavelet transforms with Fourier transforms, state properties and other special aspects of wavelets, and finish with some interesting applications such as image compression, musical tones, and de-noising noisy data.
Primer: an intro to neural networks by Z-solutions
Introduction
A very simple neural network was developed to predict the number of runs scored by a baseball team in a game based on total team offensive statistics. The resulting model could then be used to:
Compare the contribution of players to team run production based on individual statistics. Determine the key statistics and their relative importance in run production. Better identify the worth of individual players to the team for the purpose of supporting salary arbitration arguments.
The resulting neural network results were compared to a linear regression model's results. A regression model is a standard statistical tool that was used as a basis of comparison in model performance. The same data were used to develop and test both models. Game totals of the key offensive statistics such as hits, home runs and base on balls were used.
The data used for the model development consisted of a small set of 132 baseball games obtained from a realistic computer simulation based upon actual 1992 major league statistics. Both teams' data were used giving a dataset of 264 observations. A true measure of the complexity of the problem would require thousands of observations. This was beyond the resources available for this investigation. However, the number of observations available does make for an instructive problem.
Article on artificial neural networks: Making Brain Waves
Expensive and often failed AI business applications in the 1980s included mostly expert systems, computer models that attempt to re-create an expert's knowledge in a codified set of rules and statements. In the '90s, despite AI's recently tarnished reputation, neural network-based systems, some sold in shrink-wrapped packages for surprisingly little, appear to be well on their way to assuaging corporate AI aversion. "Neural networks are a dream come true," says Neal M. Goldsmith, who was instrumental in deploying American Express Co. Inc.'s AI systems in the 1980s and is now president of Tribeca Research Inc. in New York. Rule-based expert systems, such as the famous American Express "authorizer's assistant," are giving way to or being merged with models like neural networks, also known as biologically based systems for their similarity in structure to the human brain. Neural nets are gaining ascendancy because reports of their success, particularly in financial markets, have begun leaking out to the world at large. Furthermore, "neural nets are quantitative, numerical and don't require a knowledge engineer to extract expert information," says Goldsmith.
Primer and demo of atificial neural networks using baseball as example
Primer: a basic introduction to neural networks
A more advance primer on artificial neural nets....looks very good
Abstract
This report is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated, applications of neural networks like ANNs in medicine are described, and a detailed historical background is provided. The connection between the artificial and the real thing is also investigated and explained. Finally, the mathematical models involved are presented and demonstrated.
Contents:
1. Introduction to Neural Networks 1.1 What is a neural network? 1.2 Historical background 1.3 Why use neural networks? 1.4 Neural networks versus conventional computers - a comparison
2. Human and Artificial Neurones - investigating the similarities 2.1 How the Human Brain Learns? 2.2 From Human Neurones to Artificial Neurones
3. An Engineering approach 3.1 A simple neuron - description of a simple neuron 3.2 Firing rules - How neurones make decisions 3.3 Pattern recognition - an example 3.4 A more complicated neuron
4. Architecture of neural networks 4.1 Feed-forward (associative) networks 4.2 Feedback (autoassociative) networks 4.3 Network layers 4.4 Perceptrons
5. The Learning Process 5.1 Transfer Function 5.2 An Example to illustrate the above teaching procedure 5.3 The Back-Propagation Algorithm
6. Applications of neural networks 6.1 Neural networks in practice 6.2 Neural networks in medicine 6.2.1 Modelling and Diagnosing the Cardiovascular System 6.2.2 Electronic noses - detection and reconstruction of odours by ANNs 6.2.3 Instant Physician - a commercial neural net diagnostic program 6.3 Neural networks in business 6.3.1 Marketing 6.3.2 Credit evaluation
7. Conclusion
References
Appendix A - Historical background in detail Appendix B - The back propogation algorithm - mathematical approach Appendix C - References used throughout the review
FTP site with very good info on neural network construction
Part 1: Introduction
What is this newsgroup for? How shall it be used? Where is comp.ai.neural-nets archived? What if my question is not answered in the FAQ? May I copy this FAQ? What is a neural network (NN)? Where can I find a simple introduction to NNs? What can you do with an NN and what not? Who is concerned with NNs? How many kinds of NNs exist? How many kinds of Kohonen networks exist? (And what is k-means?) VQ: Vector Quantization and k-means SOM: Self-Organizing Map LVQ: Learning Vector Quantization Other Kohonen networks and references How are layers counted? What are cases and variables? What are the population, sample, training set, design set, validation set, and test set? How are NNs related to statistical methods?
Part 2: Learning
What are combination, activation, error, and objective functions? What are batch, incremental, on-line, off-line, deterministic, stochastic, adaptive, instantaneous, pattern, epoch, constructive, and sequential learning? What is backprop? What learning rate should be used for backprop? What are conjugate gradients, Levenberg-Marquardt, etc.? How does ill-conditioning affect NN training? How should categories be coded? Why use a bias/threshold? Why use activation functions? How to avoid overflow in the logistic function? What is a softmax activation function? What is the curse of dimensionality? How do MLPs compare with RBFs? What are OLS and subset/stepwise regression? Should I normalize/standardize/rescale the data? Should I nonlinearly transform the data? How to measure importance of inputs? What is ART? What is PNN? What is GRNN? What does unsupervised learning learn?
Part 3: Generalization
How is generalization possible? How does noise affect generalization? What is overfitting and how can I avoid it? What is jitter? (Training with noise) What is early stopping? What is weight decay? What is Bayesian learning? How to combine networks? How many hidden layers should I use? How many hidden units should I use? How can generalization error be estimated? What are cross-validation and bootstrapping? How to compute prediction and confidence intervals (error bars)?
Part 4: Books, data, etc.
Books and articles about Neural Networks? Journals and magazines about Neural Networks? Conferences and Workshops on Neural Networks? Neural Network Associations? On-line and machine-readable information about NNs? How to benchmark learning methods? Databases for experimentation with NNs?
Part 5: Free software
Source code on the web? Freeware and shareware packages for NN simulation?
Part 6: Commercial software
Commercial software packages for NN simulation?
Part 7: Hardware and miscellaneous
Neural Network hardware? What are some applications of NNs? General Agriculture Chemistry Finance and economics Games and gambling Industry Materials science Medicine Music Robotics Weather forecasting Weird What to do with missing/incomplete data? How to forecast time series (temporal sequences)? How to learn an inverse of a function? How to get invariant recognition of images under translation, rotation, etc.? How to recognize handwritten characters? What about Genetic Algorithms and Evolutionary Computation? What about Fuzzy Logic? Unanswered FAQs Other NN links?
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