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, the price of the bar prior to the current bar is be subtracted from the price of the current bar; the price 2 bars before the current bar is subtracted from the price 1 bar ago, etc After several such price change scores, the sampling is increased to every 2 bars, then every 4, then every 8, etc The exact spacings are in a table inside the code The rationale behind this procedure is to obtain more detailed information on very recent price behavior The further back the prices are in time, the more likely only longer-term movements will be significant Therefore, less resolution should be required Sampling the bars in this way ought to provide sufftcient resolution to detect cycles and other phenomena that range from a period of 1 or 2 bars through 50 bars or more.

Of course, when you have a le of opt-in names of customers who have agreed that they will accept promotions from your business partners, there is another valuable use of the email names you have collected. Since you are using your names more effectively, you can assume that others will be doing the same. Emails have caught on. Billions of commercial email messages are being sent every week. There is a massive market for opt-in names. If you can rent your direct-mail names 8 times a year, you can certainly rent your email names 10 times as often, or 80 times per year. The price per thousand email names varies widely and will certainly be very different from the time of writing to the time that you are reading this book. Let s take a conservative estimate of $70 per thousand for both direct-mail and email names with permission to use them for partner messages. Table 5-21 shows what you can expect to receive during a year for each name. Let s summarize again the value of a name in Table 5-22. Of course, few, if any, companies can use every one of these marketing methods, but your company can certainly use at least half of Table 5-21 Value of Name Rental

This approach is in accord with a suggestion made by Mark Jurik (jurikrescom) After assembling the 18 input variables consisting of the square-root-corrected price differences for a fact, a normalization procedure is applied The intention is to preserve wave shape while discarding amplitude information, By treating the 18 input variables as a vector, the normalization consists of scaling the vector to unit length The calculations involve squaring each vector element or price difference, summing the squares, taking the square-root, and then dividing each element by the resultant number These are the input variables for the neural network In actual fact, the neural network software will further scale these inputs to an appropriate range for the input neurons The target (dependent variable in regression terms) for each fact is simply the time-reversed Slow %K for the current bar The input variables and target for each.

By incorporating the strategies presented in this book, you can initiate a lifelong plan of aggressive nutritional practices, exercise, and stress control to support your detoxification system and stay healthy After all, what s the point of living longer, more comfortable, and more diverse lives through advances in technology if we don t have our health Thomas J Slaga, PhD..

Rental value Rentals per year CPM Revenue Commissions Net revenue Direct mail 8 $70.00 $0.56 $0.14 $0.42 Email 80 $70.00 $5.60 $1.40 $4.20

fact are written in simple ASCII format to a file that can be analyzed with a good neural network development package The resultant fact set is used to train a net to predict the time-reversed Slow %K, ie, to predict the relative position of today s close, and, it is hoped, tomorrow s open, with respect to the range of prices over the next 10 bars (a IO-bar timereversed Slow %K) The next step in developing the neural forecaster is to actually train some neural networks using the just-computed fact set A series of neural networks, varying in size, are trained The method used to select the most appropriately sized and trained network is not the usual one of examining behavior on a test set consisting of out-of-sample data.

Direct mail value Regular promotion Low-cost item promotion Last-minute special Retention messages Follow-up messages Viral marketing Newsletter Rental value Total annual value $0.80 NA NA $2.40 $1.20 NA $1.20 $0.42 $6.02 Email value $1.28 $0.32 $2.48 $3.68 $9.12 $0.24 $2.24 $4.20 $23.56

Instead, the correlation coefficients, which reflect the predictive capabilities of each of the networks, are corrected for shrinkage based on the sample size and the number of parameters or connection weights being estimated in the corresponding network The equation employed to correct for shrinkage is the same one used to correct the multiple correlations derived from a multivariate regression (see the chapters on optimization and statistics) Shrinkage is greater for larger networks, and reflects curve-fitting of the undesirable kind For a larger network to be selected over a smaller network, ie, to overcome its greater shrinkage, the correlation it produces must be sufficiently greater than that produced by the smaller network This technique enables networks to be selected without the usual reference to out-of-sample data All networks are fully trained: ie, no attempt is being made to compensate for loss of degrees of freedom by undertraining.