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10 Brilliant CSS3 and jQuery Examples
9 Feb 2010 - 1 Comment
This post is all about CSS3 and Javascript. With all the new features that come with CSS3, there are whole lot of animation possibilites that can't be achieve before. So, Check this out and get inspired
10 Stunning Examples of jQuery Animations
2 Feb 2010 - 9 Comments
jQuery can pretty much do anything you can think of. All you need is a creative imagination and some time to learn the simple and intuitive API. With several JavaScript frameworks available, jQuery is quickly becoming a favorite. If you are interested in using jQuery for animated effects, here are 10 Stunning Examples of jQuery Animation to get you on your way.
10 Best Open Source Tools for Web Designers
28 Jan 2010 - 3 Comments
Many open source tools have features comparable to expensive applications, and also free. This can save money and you can accomplish everyday tasks with having all the equipment and applications. In this article I want to share information about 10 best open source tools for web designers.
The Easiest Javascript Sliding Door Effect Tutorial with jQuery
26 Jan 2010 - 7 Comments
This is my fouth tutorial about thumbnail effect. This time is the well known sliding door effect that slide the top layer to top, bottom, left or right direction to reveal the content underneath.
Essential jQuery Tips, Plugins and Tutorials for Building a Fancy Website
20 Jan 2010 - 9 Comments
In this post, I'm going to let you know all the plugins and scripts that I used in my recent project that requires intensive AJAX calls and javascript fanciness. I have listed them in a list of plugins and tutorials and I know it's going to be really useful to all of you.
Create a Fast and Simple Toggle View Content with jQuery
19 Jan 2010 - 6 Comments
This script is fairly straight forward. What it does is using the UL list and allow user to toggle to display the content of LI items. This is a really useful user interface feature that it helps designers to save space and build a less complicated user interface.
Create a Beautiful Looking Custom Dialog Box With jQuery and CSS3
12 Jan 2010 - 6 Comments
This custom dialog box is one of the scripts in that website and I think it will be quite useful for all of us. The reason I have this custom dialog box is to overcome the inconsistencies across different browsers. It uses CSS3 to style everything and it looks really nice!
9 Rating and Voting Javascript Plugins and Tutorials
11 Jan 2010 - 1 Comment
Rating and voting features are one of the ways to encourage user interaction with the website. I have gathered a few of javascript rating plugins that are built with jQuery, Mootool or prototype framework and a few of tutorials if you want to know how it works or implement it by yourself.
10 Javascript Plugins that Skin and Beautify Web Forms
6 Jan 2010 - 8 Comments
In this post, I have found 10 javascript plugin that will able to skin the web form, they will look consistent in different browsers. Some of them skin the entire form elements and some of them skin only checkboxes and radio buttons. Anyhow, I think this is something good to know if your client want to spice up the webform a little bit more.
20 Absolutely Stylish Web Form Design Showcase
5 Jan 2010 - 2 Comments
A web form is one of the methods for users to interact with the website. We use it to collect user feedback, sign up for a service, comments and as well as entering data. It's crucial to make a clean and usable form layout to ensure it's easy to use and understand. In this showcase, you will be able to get inspired by the creativity from other web designers.
14 Outstanding jQuery Navigation Menu Tutorials
29 Dec 2009 - 10 Comments
As a jQuery lover, I always find myself trying to invent something really cool, useful and able to integrate into design easily. This time, I've chosen a few of very useful and helpful navigation menu tutorials. They are comprehensive tutorials, and cover everything with detailed explanations.
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50 Free UI and Web Design Wireframing Kits, Resources and Source Files

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http://cogweb.ucla.edu/Abstracts/AdaptedMind_92.html


 
Barkow, Jerome H., Leda Cosmides, and John Tooby (eds)
The Adapted Mind: Evolutionary Psychology and the Generation of Culture
New York, NY: Oxford University Press, 1992
 
Introduction  |  EP index  |  CogWeb
 
From the Introduction   Contents
 
The Adapted Mind is an edited volume of original, commissioned papers centered on the complex, evolved psychological mechanisms that generate human behavior and culture. It has two goals: The first is to introduce the newly crystallizing field of evolutionary psychology to a wider scientific audience.... The second goal of this volume is to clarify how this new field, by focusing on the evolved information-processing mechanisms that comprise the human mind, supplies the necessary connection between evolutionary biology and the complex, irreducible social and cultural phenomena studied by anthropologists, sociologists, economists, and historians.... With The Adapted Mind, we hope to provide a preliminary sketch of what a conceptually integrated approach to the behavioral and social sciences might look like. Contributors were asked to link evolutionary biology to psychology and psychology to culture--a process that naturally entails consistency across fields.... The central premise of The Adapted Mind is that there is a universal human nature, but that this universality exists primarily at the level of evolved psychological mechanisms, not of expressed cultural behaviors.... A second premise is that these evolved psychological mechanisms are adaptations, constructed by natural selection over evolutionary time. A third assumption made by most of the contributors is that the evolved structure of the human mind is adapted to the way of life of Pleistocene hunter-gatherers, and not necessarily to our modern circumstances.

At present, crossing [disciplinary] boundaries is often  met with xenophobia, packaged in the form of such familiar accusations as "intellectual imperialism" or "reductionism". But by  calling for conceptual integration in the behavioral and social  sciences we are neither calling for reductionism nor for the conquest and assimilation of one field or another. Theories of selection  pressures are not theories of psychology; they are theories about  some of the causal forces that produced our psychology. And theories  of psychology are not theories of culture; they are theories about some of the causal mechanisms that shape cultural forms... In fact, not only do the principles of one field not reduce to those of another, but by tracing the relationships between fields, additional principles often appear (12).

 
Contents  top

Contributors

Introduction: Evolutionary psychology and conceptual integration. Leda Cosmides, John Tooby and Jerome H. Barkow.

I. The evolutionary and psychological foundations of the social sciences

II. Cooperation III. The psychology of mating and sex IV. Parental care and children V. Perception and language as adaptations VI. Environmental aesthetics VII. Intrapsychic processes VIII. New theoretical approaches to cultural phenomena Author index
Subject index
 


 
Chapter Abstracts

Tooby, John and Leda Cosmides. The psychological foundations of culture. Barkow, Cosmides & Tooby (1992), pp. 19-136.

Abstract: we argue the following points: 1. there is a set of assumptions and inferences about humans, their minds, and their collective interaction--the Standard Social Science Model--that has provided the conceptual foundations of the social sciences for nearly a century and has served as the intellectual warrant for the isolationism of the social sciences... 2. although certain assumptions of this model are true, it suffers from a series of major defects that make it a profoundly misleading framework; these defects have been responsible for the chronic difficulties encountered by the social sciences... 3. advances in recent decades in a number of different disciplines, including evolutionary biology, cognitive science, behavioral ecology, psychology, hunter-gatherer studies, social anthropology, biological anthropology, primatology, and neurobiology have made clear for the first time the nature of the phenomena studied by social scientists and the connections of those phenomena to the principles and findings in the rest of science; this allows a new model to be constructed--the Integrated Causal Model--to replace the Standard Social Science Model.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb

Symons, Donald. On the use and misuse of Darwinism in the study of human behavior. Barkow, Cosmides & Tooby (1992), pp. 137-159.

Abstract: one of the major aims of this essay is to critically analyze the following hypothesis, which many scholars believe to be entailed by the proposition that human beings are the products of natural selection: human behavior per se can be expected to be adaptive (i.e., reproduction-maximizing), and hence a science of human behavior can be based on analyses of the reproductive consequences of human action... my primary goal in this essay is to convince the reader that because Darwinism is a theory of adaptation it illuminates human behavior only insofar as it illuminates the adaptations that constitute the machinery of behavior; describe the adaptationist program in biology; try to show how this program can be applied fruitfully to human psychological adaptations, using the perception of sexual attractiveness as an example; illustrate how social scientists, whose goal is to illuminate phenomena that are not themselves adaptations, can use evolutionary psychology to guide their research; argue that no approach to human behavior can be simultaneously psychologically agnostic and genuinely Darwinian.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb

Cosmides, Leda; Tooby, John. Cognitive adaptations for social exchange. Barkow, Cosmides & Tooby (1992), pp. 163-228.

Abstract: evolutionary biologists have analyzed the conditions under which adaptations for engaging in cooperative behavior can be expected to evolve; these analyses show that cognitive mechanisms for engaging in cooperation can be selected for only if they solve certain complex information-processing problems; to solve these problems efficiently, the cognitive mechanisms involved must have certain specific design features; review experiments that have been conducted to see whether these predicted design features actually exist.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb

McGrew, W. C., and Anna T. C. Feistner. Two nonhuman primate models for the evolution of human food sharing: Chimpanzees and callitrichids. Barkow, Cosmides & Tooby (1992), pp. 229-243.

Abstract: summarize briefly current knowledge of food sharing in other primates; focus on the two types of primates who engage in habitual food sharing--chimpanzees and callitrichids (marmosets and tamarins); use Isaac's (1978) 10 features of food sharing to contrast Homo sapiens with our nearest living relations and to contrast chimpanzees and callitrichids; these features will be reordered, modified, corrected, and updated, whenever necessary, in an effort to say whether the human-nonhuman contrasts are ones of kind or only of degree; return to the themes of the social and the technological, in an attempt at synthesis.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb

Buss, David M. Mate preference mechanisms: Consequences for partner choice and intrasexual competition. Barkow, Cosmides & Tooby (1992), pp. 249-266.

Abstract: examines four fundamental premises: (a) human mate preference mechanisms are central psychological procedures that affect actual mating decisions, (b) mate preferences exert a powerful selection pressure on human intrasexual competition, (c) there exists a class of acts generated by each preference mechanism, and (d) the evolution of psychological mechanisms cannot be understood fully without identifying the class of acts generated by each psychological mechanism.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb
 
Ellis, Bruce J. The evolution of sexual attraction: Evaluative mechanisms in women. Barkow, Cosmides & Tooby (1992), pp. 267-288.

Abstract: argue ...that general principles guiding female mate preferences can be discerned at the appropriate level of abstraction and that the evolution-based concept of "mate value" (Symons 1987a) provides a useful heuristic in this endeavor; review the psychological literature on male sexual attractiveness in order to see whether women find traits that would have signaled high mate value in our natural environment attractive in men... status (economic status, ornamentation, dispositional characteristics, willingness to invest, structural powerlessness); physical dominance (high-dominance personality, body language, physiognomy, height); mate choice paradox.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb
 
Wilson, Margo and Martin Daly. The man who mistook his wife for a chattel. Barkow, Cosmides & Tooby (1992), pp. 289-322.

Abstract: argue that sexually proprietary male psychologies are evolved solutions to the adaptive problems of male reproductive competition and potential misdirection of paternal investments in species with mistakable paternity; describe the complex interrelated design of mating and paternal decision rules in some well-studied avian examples; consider the peculiarities of the human species in this context; characterize some features of human male sexual proprietariness, contrasting men's versus women's perspectives and actions; review some of the diverse consequences and manifestations of this ubiquitous male mindset.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb
 
Profet, Margie. Pregnancy sickness as adaptation: A deterrent to maternal ingestion of teratogens. Barkow, Cosmides & Tooby (1992), pp. 327-365.

Abstract: expand and refine an earlier argument (Profet, 1988): the food aversions, nausea, and vomiting of pregnancy sickness evolved during the course of human evolution to protect the embryo against maternal ingestion of the wide array of teratogens (toxins that cause birth defects) and abortifacients (toxins that induce abortion) abundant in natural foods; argue that pregnancy sickness represents a lowering of the usual human threshold of tolerance to toxins in order to compensate for the extreme vulnerability of the embryo to toxins during organogenesis (the embryonic period of organ differentiation and maximum vulnerability to teratogens).

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb
 
Mann, Janet. Nurturance or negligence: Maternal psychology and behavioral preference among preterm twins. Barkow, Cosmides & Tooby (1992), pp. 367-390.

Abstract: explore some of the psychological mechanisms involved in maternal decisions to care for and invest in high-risk offspring; attempt to integrate psychological and evolutionary approaches to the study of child neglect by examining the relationship among maternal psychology, maternal behavior, and infant health characteristics in detail among a small population of extremely low-birth-weight (ELBW) preterm twins.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb
 
Fernald, Anne. Human maternal vocalizations to infants as biologically relevant signals: An evolutionary perspective. Barkow, Cosmides & Tooby (1992), pp. 391-428.

Abstract: argue that the characteristic vocal melodies of human mothers' speech to infants are biologically relevant signals that have been shaped by natural selection... discuss the current debate about the status of human language as an evolved mechanism, as a way of examining criteria for when it is appropriate to invoke natural selection as a causal explanation for the evolution of human behavior; examine the antiadaptationist argument that language is an "exaptation" and therefore not a product of selection, as well as the argument that adaptive features must be designed for optimal efficiency... the characteristics of mothers' speech to infants are described in some detail, along with research on the communicative functions of intonation in infant-directed speech; examine infant-directed speech in the context of ethological research on vocal communication and maternal behavior in nonhuman primates, in order to identify selection pressure relevant to understanding the adaptive functions of human maternal speech.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb
 
Boulton, Michael J., Peter K. Smith. The social nature of play fighting and play chasing: Mechanisms and strategies underlying cooperation and compromise. Barkow, Cosmides & Tooby (1992), pp. 429-444.

Abstract: suggest that there are important advantages in taking an evolutionary approach to the study of r/t (rough & tumble play); evidence suggests that this form of play may be universal to all human cultures and that, as such, it may have been shaped by natural selection acting on physiological and psychological mechanisms, in order to provide some specific benefits; for humans, there is some evidence ...that the benefits may be related to intraspecific fighting (with hunting and predator avoidance as less likely candidates); other considerations suggest that these and other mechanisms that generate r/t have ensured that it is a cooperative activity in which participants with differing needs are willing to compromise.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb
 
Pinker, Steven and Paul Bloom. Natural language and natural selection. Barkow, Cosmides & Tooby (1992), pp. 451-493.

Abstract: argue that there is every reason to believe that language has been shaped by natural selection as it is understood within the orthodox "synthetic" or "neo-Darwinian" theory of evolution (Mayr, 1982); argue ...that language is no different from other complex abilities such as echolocation or stereopsis and that the only way to explain the origin of such abilities is through the theory of natural selection... examine arguments from evolutionary biology about when it is appropriate to invoke natural selection as an explanation for the evolution of some trait; apply these tests to the case of human language, and conclude that language passes; examine the motivation for the competing nonselectionist position and suggest that they have little to recommend them; refute the arguments that have claimed that an innate specialization for grammar is incompatible with the tenets of a Darwinian account and thus that the two are incompatible.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb
 
Shepard, Roger N. The perceptual organization of colors: An adaptation to regularities of the terrestrial world? Barkow, Cosmides & Tooby (1992), pp. 495-532.

Abstract: consider some characteristics of the perception and representation of colors that, although not universal in animal vision, do appear to be universal in the normal color vision of humans, prevalent in other primates, and common in a number of other quite different but also highly visual species, including the birds and the bees... questions raised are (a) whether these characteristics of color perception and representation are merely arbitrary design features of these particular species, (b) whether these characteristics arose as specific adaptations to the particular environmental niches in which these species evolved, or (c) whether they may have emerged as advanced adaptations to some properties that prevail throughout the terrestrial environment.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb
 
Silverman, Irwin and Marion Eals. Sex differences in spatial abilities: Evolutionary theory and data. Barkow, Cosmides & Tooby (1992), pp. 533-549.

Abstract: (maintain) that the critical factor in selection for spatial dimorphism in humans was sexual division of labor between hunting and gathering during hominid evolution; extend the premise to propose that if these attributes evolved in males in conjunction with hunting, spatial specializations associated with foraging should have, correspondingly, evolved in females; describe a series of studies exploring these hypothesized female spatial specializations.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb
 
Orians, Gordon H. and Judith H. Heerwagen. Evolved responses to landscapes. Barkow, Cosmides & Tooby (1992), pp. 555-579.

Abstract: discuss evidence relevant to the "savanna hypothesis"--the hypothesis that we have evolved preferences for habitats with features characteristic of a high-quality tropical African savanna, the environment in which the human lineage is thought to have initially evolved; develop a task analysis, or computational theory, specifying what kinds of decisions our ancestors would have had to make in the course of habitat selection and what kinds of environmental cues would have been reliably associated with habitat quality during the Pleistocene... propose that habitat selection proceeds in three stages; each of these stages should be characterized by different cognitive and affective processes.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb
 
Kaplan, Stephen. Environmental preference in a knowledge-seeking, knowledge-using organism. Barkow, Cosmides & Tooby (1992), pp. 581-598.

Abstract: begins with an analysis of the role of information in human evolution; wayfinding is then introduced as an important activity of early humans with interesting informational properties; discussion of an extensive program of research on human environmental preference will serve as a window on the motivational inclinations that encourage the acquisition and utilization of wayfinding information.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb
 
Nesse, Randolph M. and Alan T. Lloyd. The evolution of psychodynamic mechanisms. Barkow, Cosmides & Tooby (1992), pp. 601-624.

Abstract: argue not that psychoanalytic concepts will turn out to exactly match the evolved functional subunits of the mind, but only that they offer the best available starting point; examine a variety of phenomena that are well accepted by psychoanalysts--repression, psychological defenses, intrapsychic conflict, conscience, transference, and childhood sexuality--in order to compare their characteristics to the predictions made by various hypotheses about their possible functions.

 
I.  |  II  |  III  |  IV  |  V  |  VI  |  VII  |  VIII  |  top  |  EP Bibliography  |  EP index  |  CogWeb
 
Barkow, Jerome H. Beneath new culture is old psychology: Gossip and social stratification. Barkow, Cosmides & Tooby (1992), pp. 627-637.

Abstract: the concepts of vertical integration and evolutionary psychology ...suggest that there is little human that is really "evolutionarily unanticipated"; the two "biologically unanticipated" social phenomena this chapter deals with are (a) gossip, soap operas, and "celebrities"; and (b) social stratification; I have deliberately chosen disparate examples of evolutionary novelty in order to illustrate how the evolving field of evolutionary psychology permits powerful, vertically integrated explanations of major sociocultural phenomena.

 
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http://www.trakkcor.ch/index.html

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06/29/2008 feature release: trakkcor-0.3 (GPRMC support)
09/16/2007 feature release: trakkcor-0.2.0-beta (live tracking)
02/08/2007 patch release: trakkcor-0.1.1
11/26/2006 first binary release: trakkcor-0.1
11/24/2006 source available on sourceforge.net

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I've written a textbook entitled "Machine Learning: An Algorithmic Perspective". It will be published by CRC Press, part of the Taylor and Francis group, on 2nd April 2009. The book is aimed at computer science and engineering undergraduates studing machine learning and artificial intelligence.
There are lots of Python code examples in the book, and the code is available here. Where special datasets are used they are provided with the code, and there are links to additional datasets at the bottom of the page.

Option 1: Eclipse zip file of all code
Option 2: Choose what you want from here:
Many of the datasets used in the book are available from the UCI Machine Learning Repository.
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DIANE + HUNTER

NEW YEAR'S EVE WEDDING | FAIRMONT HOTEL | CHICAGO, ILLINOIS

Wow, what an incredible wedding Diane & Hunter had!! Within 10 minutes of meetings Diane, I knew several things were for sure….one, this would be an incredible wedding…two, it would ooooze with style…and three, we would have the kind of relationship that just clicked and was awesome!! I was totally right….and I can’t tell you how much I loved being a part of this wedding!! Combine downtown Chicago, New Years Eve, One stunning and incredible couple and fun wedding party, a stellar, uber stylish venue and top it off with one crazy awesome wedding dress…and you have a recipe for magic. We had such a fun day….starting off with the rehearsal, through a fun day of pictures with the bride & groom and wedding party, to a beautifully sweet ceremony and into one heck of an awesome party that brought in the New Year! It was all just terrific and truly a special thing to be a part of!!! Diane & Hunter…I just adore you both soo much!! Thank you for allowing me to be a part of your amazing wedding!! XO! :)

VENDORS:

Ceremony & Reception: Fairmont Hotel Chicago
Design & Florals: www.kehoedesigns.com
Dress: Ultimate Bride Chicago
Hair: Alison Haiduke
Makeup: Faces by Suzanna, Suzanna Ishoo
Quartet: Parkwest Strings
DJ: DJ Sye Young


In the Presidential Suite….in the President’s hometown… :)

Another very sweet first meeting…

I ADORE this series soooo much!!! Couldn’t decide on the black and white or sepia…loved them both…so you get them both! :)


And we were off to the Train Station because it was a windy, take your breath away 12 degrees outside in Chicago…and had to be in the double negatives with the windchill! Yikes!

I can’t believe Diane endured this….she’s such a trooper! I was totally scared I would have my first bride with frostbite!

One amazing cocktail hour…complete with incredible food and LOTS of it…which continued through the end of the night with meals, snacks and desserts galore! Oh soo tempting for a pregnant chick! ;)

And then the stunning reception…

Hunter and Diane did the countdown! HAPPY NEW YEAR 2010!!!!!!!

After the incredible cocktail hour, then salad and a full dinner, then cake, then a full dessert and fruit bar, they had a late night snack to complete the night!

DIANE + HUNTER

REHEARSAL DINNER | NEW YEAR'S EVE WEDDING | CHICAGO, ILLINOIS

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Forecasting with SQL

By Mark Wojciechowicz, 2010/02/09

Total article views: 3409 | Views in the last 30 days: 3409

Introduction

Forecasting is a very common business process, with which most companies address in a full blown demand planning system. The benefit of such a system is that many different forecast models can be tested for the best fit and applied appropriately through the life cycle of a product. However, it is sometimes necessary to perform forecasting or elements of forecasting outside the demand planning system, generally for ad hoc analysis.

So, if you are ever given the task of creating a time series forecast, with T-SQL as your tool, you will probably start by using one of the most common forecast models: simple linear regression with trend and seasonality. This forecast method is also known as the Holt-Winter’s model and its application will be the subject of this article. One take away is the demonstration of forecasting, though another useful aspect is the illustration of the math to calculate a trend line for a chart.

Simple Linear Regression

Simple linear regression finds the slope (or trend line) within a time series and continues that slope forward to predict a future outcome. The seasonality is then derived from the historical data and applied to the future trend. Seasonality, or periodicity, is a regular fluctuation in demand. For example, the month of December may spike due to holiday sales and August may slump because of the travel season. This could be expressed as a percent variance from the trend line.

Time series forecasts are typically created at a monthly level and broken down to a weekly or daily level, as needed. The monthly breakdown may simply be a function of a weekly mask (% for each week of the month) or it may be a something more complicated as an annual mask. More often than not, it is structured to a fiscal time period. Forecasters prefer the monthly altitude because it is a high enough level for good forecast accuracy while still low enough to detect seasonality and be useful to the business community. For example, think of the challenge in forecasting one item at the ship-to level for each day of the year as compared to forecasting sales for the entire business for the whole year with no ship-to break out. The latter annual forecast will certainly win in overall accuracy.

One last note before getting started, Analysis Services (SSAS) offers a couple of great tools for forecasting that would be infinitely easier to use, if the environment is already built. The first are linear regression functions that are part of Multidimensional Expressions (MDX). These functions pared with others could easily replicate the steps below, in a much simpler way (provided you are comfortable with MDX). However, these functions are broken into pieces to add flexibility in how they are put together, allowing for an additive regression vs. multiplicative, for example. The formulas below should help make clearer the intent of these functions.

The second is the forecasting model that is part of the data mining tools in SSAS. Microsoft uses an algorithm it refers to as Auto Regressive Tree with Cross Prediction (ARTXP), which essentially acts like linear regression, but corrects itself very quickly. By correction I mean that if you were to follow a trend that was steadily growing for 3 years and then suddenly the data started to decline very rapidly, the linear regression trend line will take more than a few periods to start to acknowledge this change and get on the right path again. Often, some sort of moving average like Auto-Regressive Integrated Moving Average (ARIMA) or exponential smoothing will follow this pattern better. The data mining forecast tool uses ARIMA to correct the trend line quickly. Also, ARTXP can use cross correlation (with Enterprise Edition) to detect relationships across products. This could be used to inform the forecast, when an increase in one product might cannibalize the sales of another.

The Forecast Process

This forecast process has three major parts as follows:

1. Prepare the historical data

2. Find the slope and seasonality

3. Create the future forecast and apply seasonality

Step 1 – Prepare the Historical Data

In step one, the historical data is prepared by cleansing it of abnormalities. This is typically done by:

  • removing promotional effects,
  • smoothing (or de-seasonalizing) the data.

Promotions generally cause a dip before and after the promotional period and inflated sales during the promotion. It is not desirable to have this information in the sales history, because promotions are generally non-repeating events – they will likely not be to the same customer, for the same item during the same time each year for the same amount off with the same ad support, etc.

A wise old forecaster tipped me off one day that the best way to remove promotions is by using a central moving median (this, as opposed to a moving average). A “central” moving median or average uses a certain number of periods before and after a given period. A normal moving average uses the periods before a given period to find the average. The effect is that the average will trail behind an event, no matter the number of periods used. The “median” is simply the middle value in a set of data. A median is a great choice because it cuts across the data where an average will generally show some sign of a spike, however subdued. Figure 1 illustrates the difference – note how the moving average (the red line) rises after the event in week 26, but the median (the green line) cuts straight through.

Figure 1

Moving media v avg

Lastly, the smoothing (or de-seasonalizing) process uses the central moving average. In this case, we wish to retain the seasonal influence but dampen the fluctuations in the data. De-seasonalizing is done so that a better trend line can be extracted from the time series.

In the example (available for download), I start at the point in the process when promotional effects have been removed and the data has been aggregated to a monthly level. I start by creating a table variable to store the results. The table has the following columns:

  • Forecastkey (integer, not unique)
  • Year (integer)
  • Month (integer)
  • Product (varchar)
  • Baseline Quantity (integer)
  • Smoothed Quantity (integer)
  • Trend (Numeric(38,17))
  • Seasonality (Numeric(38,17))
  • Forward Trend (Numeric(38,17))
  • Forecast (Numeric(38,17))

The forecastkey is simply a value that increments by 1 for each period record for an item. It starts over for each new item. This column is used for the math in the linear regression – it is very important that the table is ordered by Product, Year, Month, so that the forecastkey has the correct incremental values. Year and month are used, but in a practical application, these would be replaced by a datekey tied to a time dimension.

The baseline quantity is the sales amount stripped of promotions. The smoothed quantity is the baseline amount, smoothed by a central moving average. The trend is the trend line for the historical period. Seasonality is calculated as a ratio of the baseline amount to the trend. The forward trend does not need to be included, but is there to illustrate the creation of a forward trend line. Lastly, the forecast is the forward trend multiplied by the seasonality.

The last four columns have a huge numeric value to avoid rounding errors that could arise during the calculations of these columns. They do not, by any means, imply the level of forecast accuracy. In spite of everything, all forecasts are wrong. Good forecasts are just less wrong.

In the first step, we insert the historical data into the table variable and then update the smoothed_quantity column with a central moving average. We find the average by joining the table to itself on product and forecast key. Remember that the forecastkey is simply and integer that increments by one for each period for a product. In the code snippet below, we join by finding the difference between the keys for a range from -3 to 3. This means that we are looking at 3 periods before and after the period that we are updating and finding the average of all of these periods.

Code snippet:

 Update
       @ForecastTable
 SET
       Smoothed_Quantity = MovAvg.Smoothed_Quantity
 FROM(
       SELECT
             a.ForecastKey as FKey,
             a.Product as Prod,
             Round(AVG(Cast(b.baseline_Quantity as numeric(14,1))),0) Smoothed_Quantity
       FROM
             @ForecastTable a
       INNER JOIN
             @ForecastTable b
       ON
             a.Product = b.Product
            AND  (a.ForecastKey - b.ForecastKey) BETWEEN-3 AND 3
       GROUP BY
             a.ForecastKey,
             a.Product) MovAvg
 WHERE
       Product = MovAvg.Prod
       AND ForecastKey = MovAvg.FKey

The smoothed quantity will help us find a more reliable slope in the data that will be less influenced by outlying data points.

Step 2 – Finding the Slope

In step two, we find the slope and calculate historical seasonality. This is done by solving the equation y = a + bx. This looks a lot like y = mx + b and I really have no idea why the variables are flipped around in this equation, but the former is what is used in text books for linear regression. If you remember back to high school trig, you may recall the terms “rise” and “run” in reference to the slope of a line. In the equation above, the slope (run) is “b” and the y-intercept (or rise) is “a.” A really great resource to explain the rest of the math is http://www.easycalculation.com/statistics/learn-regression.php. The SQL statement below illustrates how this formula can be solved with SQL.

The formulas to solve “a” and “b” are as follows:

  • Slope(b) = (Count * SXY - (SX)(SY)) / (Count * SX2 - (SX)2)
  • Y-Intercept(a) = (SY - b(SX)) / Count

where S means “the sum of.”

To accomplish this task in SQL, I created a second table variable to complete the calculations and store them by product. By later joining this table variable to the first table variable which holds the data that we will forecast, we avoid using a cursor and looping through one item at a time. This new table variable has the following columns:

  • Product (nvarchar(25)) – the product identifier
  • Counts (int),
  • SumX (Numeric(14,4)),
  • SumY (Numeric(14,4)),
  • SumXY (Numeric(14,4)),
  • SumXsqrd (Numeric(14,4)),
  • b (Numeric(38,17)),
  • a (Numeric(38,17))
Column Name Data Type Description
Product Varchar(25) Product identifier
Counts int The count of all historical rows for an item
SumX Numeric(14,4) The sum of the forecastkey. You can think of this as a representation for time which would place on the X-axis if we charted it.
SumY Numeric(14,4) The sum of the smoothed quantity (of the value for our Y-axis). This is the historical value that we are creating the forecast for.
SumXY Numeric(14,4) The sum of (the forecastkey * the smoothed quantity)
SumXsqrd Numeric(14,4) The sum of the forecastkey to the power of 2
b Numeric(38,17) The Slope (the formula will follow in more detail below)
a Numeric(38,17) The y-intercept (the formula will follow in more detail below)

The first column identifies the product – one calculation will be stored for each item and applied across all historical rows. The next 5 columns are used to calculate “b” and “a” from the formula y = a + bx. Later, when we calculate the forecast, “x” will be the forecastkey.

This 2nd step in the forecast process is achieved in three substeps. First, the calculations for Counts through SumXsqrd are solved:

Code Snippet

SELECT
       Product,
       COUNT(*),
       sum(ForecastKey),
       sum(Smoothed_Quantity),
       sum(Smoothed_Quantity * ForecastKey),
       sum(power(ForecastKey,2))
 FROM
       @ForecastTable
 WHERE
       Smoothed_Quantity IS NOT NULL
 GROUP BY
       Product

In the code snippet above, product will be our key for joining to the historical table. The “where” clause is used to ignore rows without a smoothed quantity. This can be useful if the forecast is stored in a permanent table and will be updated each month as actuals arrive into the table. In other words, you would not want to include forecast rows in your calculation.

Next, we calculate “b” using the calculations we just made:

b = ((tb.counts * tb.sumXY)-(tb.sumX * tb.sumY))/(tb.Counts * tb.sumXsqrd - power(tb.sumX,2))

In high school, we calculated a slope between two points. This calculation calculates a slope through an array of points. This is the reason why smoothing of the historical data is so important, because it helps to limit the effect of outliers and improve the accuracy of the slope.

Finally, we calculate “a” with the following:

a = ((tb2.sumY - tb2.b * tb2.sumX) / tb2.Counts)

This calculation finds the point where our slope from above will intersect with the Y-Axis. This is important because it gives reference to the starting point of a forecast – are we selling 10 a month or 10,000? The y-intercept will be used in our forecast calculation to place the total quantity in the right range.

Step 3 – Creating the Future Forecast

In the last step, we create a loop to insert future forecast records by joining the historical data with the calculations table variable. In this exercise, we will forecast 12 periods ahead, so we loop 12 times, each time inserting a row for each product and incrementing the forecastkey by 1. We find the last forecastkey by finding the max(forecastkey) for each product.

The next columns that we insert are the year and month. If this were a dataset that we would add a new forecast row each month, we would want to find the year and month dynamically which could be done by adding a column to carry a date for each first of the month. Then the year would be Year(DateCol) and the month Month(DateCol). Alternatively, if these were fiscal periods, you could find these future periods by joining to a date table.

After we add the month and year (and product) to analyze out data, we calculate the trend (or forecast without seasonality) and we calculate the forecast with seasonality. The trend is not essential to the process, but it is nice to include in charting to more clearly demonstrate the direction of the forecast. To calculate the trend, we return to the formula y = a + bx:

MAX(A)+ (MAX(B)* MAX(Forecastkey)+ 1)

The max() function is only used because we need to fine the last forecastkey for each item, it has no effect on the values for “a” and “b” from our calculation table. To find the forecast, we use the same formula as above and multiply this by the average seasonality for the item:

Code Snippet:

MAX(A)+ (MAX(B)* MAX(Forecastkey)+ 1)
 * 
 (SELECT
       Case WHEN avg(Seasonality)= 0 THEN 1 ELSE avg(Seasonality)END
   FROM
       @ForecastTable SeasonalMask
   WHERE
       SeasonalMask.Product = a.Product
      AND SeasonalMask.CMonth = @Loop +1)

This sub-select statement returns the average for the seasonality column for the particular month that we are forecasting for. The case statement eliminates making the forecast 0 if the average trend turns out to be 0 for some reason. Lastly, we review the results. I suggest playing with the data in excel to see how results came out. Figure 2 shows a snapshot of this.

Figure 2

Linear Regression Example

Summary

This article demonstrates one of the most common forecast models – linear regression, and shows how to apply the forecast formulas in a SQL environment. While users could perform these same formulas in excel, this exercise offers an alternative by creating a database driven forecast. While no forecast model is a one size fits all for every application, linear regression provides a window into forecasting and offers a level of sophistication beyond moving averages. This code could be used for forecasting sales data or even simply used for showing a trend line of data in a chart or for creating ad hoc forecasts.

Resources:

Example - Performing Linear Regression with SQL.sql

By Mark Wojciechowicz, 2010/02/09

Total article views: 3409 | Views in the last 30 days: 3409
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Ellison, N., Heino, R. and Gibbs, J. Managing Impressions Online: Self-Presentation Processes in the Online Dating Environment. Journal of Computer-Mediated Communication, 11 (2). (Spring 2006)
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McKenna, K.Y.A. and Green, A.S. Virtual Group Dynamics. Group Dynamics: Theory, Research, and Practice, 6 (1). 116--127. (2002)
 
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Norris, P. The Bridging and Bonding Role of Online Communities. Press/Politics, 7 (3). 3--13. (2002)
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Resnick, P. Beyond Bowling Together: SocioTechnical Capital. in Carroll, J. ed. HCI in the New Millennium, Addison-Wesley, 2001.
 
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Shoemaker, P.J. Hardwired for news: Using biological and cultural evolution to explain the surveillance function. Journal of Communication, 46 (3). (1996)
 
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Stutzman, F., An Evaluation of Identity-Sharing Behavior in Social Network Communities. in iDMAa and IMS Code Conference, (Oxford, OH, 2005).
 
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Wellman, B., Haase, A.Q., Witte, J. and Hampton, K. Does the Internet Increase, Decrease, or Supplement Social Capital? Social Networks, Participation and Community Commitment. American Behavioral Scientist, 45 (November 2001).

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