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versions of X , which implies that the initialization stage has to be approximated. More details can be found in Delbeke and Abry [27] and Veitch and Abry [75]. The fast pyramidal algorithm is not only scalable because of its linear complexity, O n for data of length n, but is simple enough to implement on-line and in real time in high-speed packet networks. An on-line wavelet-based estimation method for the scaling parameter with small memory requirements is given by Roughan et al. [62]. 2.2.2 Scaling Processes: Self-Similarity and Long-Range Dependence



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84. Impact on online sales 85. Impact on of ine sales 86. Discount redemption rate 87. Impact on other of ine behavior (e.g., TV tune-in) 88. Leads generated 89. Products sampled 90. Visits to store locator pages 91. Conversion change due to user ratings, reviews 92. Rate of customer/visitor retention 93. Impact on customer lifetime value 94. Customer acquisition/retention costs through social media 95. Change in market share 96. Earned media s impact on results from paid media 97. Responses to socially posted events 98. Attendance generated at in-person events 99. Employees reached (for internal programs) 100. Job applications received Happy now Good. If, on the other hand, you actually want to know if any of these metrics are useful and how to use them then you ll need to heed David s advice: Ultimately, you need to start with guring out your business objectives and then apply these metrics accordingly.





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We can de ne scaling behavior broadly as a property of scale invariance, that is, when there is no controlling characteristic scale or, equivalently, when all scales have equal importance. There is no one simple de nition that can capture all systems or processes with this property; rather there are a set of known classes open to

Fig. 2.1 Fast lter-bank-based pyramidal algorithm. The DWT can be computed using a fast pyramidal algorithm: that is, given that we have approximation aX j 1; k at level j 1, we obtain approximation aX j; k and detail dX j; k at level j by convolving with h1 and g1, respectively, and decimating. The coef cients of the lters h1 and g1 are derived from the chosen scaling function and wavelet f0 and c0 . The downarrow stands for a decimation by a factor of 2 operation: one drops the odd coef cients. An initialization step is required to go from the process X to the approximation of order 0: aX 0; k .

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expansion. In this section we brie y introduce the most well known of these, namely, self-similar, self-similar with stationary increments, and long-range-dependent processes. Please note that throughout this chapter we will use the following convention: f x $ g x as x 3 a means that limx3a f x =g x 1, and f x % g x as x 3 a means that limx3a f x =g x C, where C is some nite constant. Recall that a process X fX t , t P Rg is self-similar with parameter H > 0 H-ss if X 0 0 and fX ct , t P Rg and fcH X t , t P Rg have the same nitedimensional distributions. Such a process, obviously, cannot be stationary. The process X is H-sssi if it is H-ss and if, in addition, it has stationary increments, that is, if the nite-dimensional distributions of its increments fX t h X t , t P Rg do not depend on t. An H-sssi process with H < 1 has zero mean and a variance that behaves as EX 2 t s2 jtj2H . The fractional Brownian motion (FBM), for example, is the (unique) Gaussian H-sssi process, which is simply Brownian motion for H 1. 2 Long-range dependence,1 on the other hand, is associated with stationary processes. A stationary nite-variance process X displays long-range dependence if its spectral density GX n satis es GX n $ cf jnj a as n 3 0; 2:6

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