I am developing a rails application where I need a "success rate" system similar to RetailMeNot. I noticed that they use jQuery Sparkline library (http://omnipotent.net/jquery.sparkline/) to generate a success rate trend for each coupon.
For example, in their source code:
<em>84%</em> Success<br/><span class="trend">14,18,18,22,19,16,15,28,21,17</span>
<em>20%</em> Success<br/><span class="trend">-1,1,-1,-1,-2,-2,1,-1,1,-1</span>
Can someone explain to me the best way to develop a similar trending system for success rate?
A trend is just a number calculated at regular intervals. In this case it looks like the site is just binning the data they get from the "Did this coupon work for you?" question, and then plotting those values in the chart. In other words, they take the number of (successes - failures) in some time interval (e.g. 12 hours) and plot that number for each interval.
As time passes, they probably rebin to keep the number of bars on the x axis acceptable. For example, if they only want to show 8 bars on the plot, then after 4 hours they'll have to widen the bins.
Related
I run daily a job. Today, that job takes 1:45:09 hrs. I have a lot of such durations for that job from the past weeks and I want to be able to show that graphically using a simple column chart. On the Y axis I want duration ticks from 0:00:00 - 5:00:00 or so that I can easily compare the runtimes from the past weeks and see if the job is gradually taking longer and longer.
I have read and implemented a lot of answers from StackOverflow and other internet resources but none of them fit my purpose. When using unix timestamps (since 1970, etc) I get columns that are all of the same hight and Y-axis ticks in years instead of hours from 1970 to now.
Another option was to just calculate the minutes or seconds. Then the difference become appearant, but instead of time elements in the Y axis and tooltips I get integers.
Can someone show me how to achieve my goal in a fiddle? The question looks common enough to me for any monitoring software.
-- EDIT --
Here is a Photoshop sample of what I am trying to achieve:
On the Y-axis: a time scale. In the tooltip: date, objectname and time taken.
-- END EDIT --
BTW, I have no chart type preference. The usual column charts just seem to fit the purpose.
Thanks for any help!
I’ve got a statistical/mathematical problem I’m stumped on and I was really hoping to get some help. I’m working on a research where I need to compare a weekly graph with its own history to see when in the past it was almost the same. Think of this as “finding the closest match”. The information is displayed as a line graph, but it’s readily available as raw data:
Date...................Result
08/10/18......52.5
08/07/18......60.2
08/06/18......58.5
08/05/18......55.4
08/04/18......55.2
and so on...
What I really want is the output to be a form of correlation between the current data points with the other set of 5 concurrent data points in history. So, something like:
Date range.....................Correlation
07/10/18-07/15/18....0.98
We’ll be getting a code written in Python for the software to do this automatically (so that as new data is added, it automatically runs and finds the closest set of numbers to match the current one).
Here’s where the difficulty sets in: Since numbers are on a general upward trend over time, we don’t want it to compare the absolute value (since the numbers might never really match). One suggestion has been to compare the delta (rate of change as a percentage over the previous day), or using a log scale.
I’m wondering: how do I go about this? What kind of calculation I can use to get the desired results? I’ve looked at the different kind of correlation equations, but they don’t account for the “shape” of the data, and they generally just average it out. The shape of the line chart is the important thing.
Thanks very much in advance!
I would simply divide the data of each week by their average (i.e., normalize them to an average of 1), then sum the squares of the differences of each day of each pair of weeks. This sum is what you want to minimize.
If you don't care about how much a graph oscillates relative to its mean, you can normalize also the variance. For each week, calculate mean and variance, then subtract the mean and divide by the root of the variance. Each week will have mean 0 and variance 1. Then minimize the sum of squares of differences like before.
If the normalization of data is all you can change in your workflow, just leave out the sum of squares of differences minimization part.
I'm facing a following problem. In Kibana 4 I've created a line chart based on my input from elasticeasrch but I can only display average, min, max instead of an actual value of the field per time, e.g. sent bytes.
Most answears to that question on stackoverflow are about Kibana 3 (How to create value over time chart with Kibana 3?) and seem to include a Histogram on a X axis, yet I can't seem to find one which will enable me to apply them to Kibana 4. I was unable to find the histogram panel and once I click on the discover tab there is the constant Searching loading.
If I have the following fields in my _source:
{"timestamp":"2015-06-02T10:16:44.0855","time":587,"threadName":"Thread Group 1-957","byte":1372,"status":"false","latence":306,"registerCall":"404"}
and I would like to have the number of bytes on the Y-axis and on the X-axis my timestamp.
Any help in the right direction will be appreciated :)
To create a value over time line chart in Kibana, follow these steps:
Go to visualize tab and select line chart
In the X-axis, select X-axis, Aggregation as Date Histogram and then select your timestamp field as the date field.
Next for the Y-Axis, select Sum as the aggregation and then bytes as the field.
For the X axis, what Alcanzar said is good, but as you notice, the Y axis is problematic.
Sum (suggested by "Limit") works, but since it's aggregated, it shows the total used in each aggregated bucket, but that may be meaningless depending on what you are trying to show. Your question isn't clear on what you want, so I'm just guessing here. One hour of requests, each of which ran for one minute and sent 1 megabyte is indeed 60 megabytes-minutes, if you are trying to show total capacity used over than hour (maybe you are paying a bill based on usage per time). On the other hand, if you are trying to show peak usage in each time, it would be wrong.
You said you already looked and Max and Min and they don't meet your needs. I don't suppose Standard Deviation would be any better?
I have the same concern. The best I've been able to do so far is
display Min and Max simultaneously in the Y axis. When they diverge, I know I'm zoomed out too far, so I zoom in until they align.
This is how I know I'm seeing individual events.
In any case, I share your frustration. I too would like to be able to show time series as easily as I can in, say, Excel.
I need to calculate the nearest dataPoint in a time series chart from a specific point in a chart
I obviously cannot use d=sqrt(x*x+y*y) as my x axis is in time series, hence it wont make sense to have an equation where I am adding distance and time together (x,y need to have same units). Moreover visually it may seem right, but it still depends upon the scale of the x axis.
So what best logic can I use to find the nearest point?
I can think of using a quadratic form of x (i.e. time) so as that my final function can ne f(x*x,y), but then it is just a subjective equation.
Does anyone have a better and more logical approach to this. If there is an intuitive logical approach I will love it. And if there is a complicated model I would still like to know about it and explore it.
Thanks
EDIT
TO give background: I am polling people to predict where the stock price will be in April(they have to mention exact date when the expect price to be there) ... How do I measure their performance?
One intuitive way is by calculating the average absolute change per day.
i.e.
Sum of Absolute changes every day from previous day / Total number of days in series.
Thereafter I can translate each day in terms of prices i.e. the average price change per day.
Thus if average absolute change per day is lets say 2, then a price that is 10 days away can be said to be 20 price points away.
Thereafter I can calculate the distance based on sqrt(x*x+y*y) formula.
This can be fine tuned by using a bell curve (std dev and mean) rather than just mean of absolute change per day. But then it will make solution more ocmplicated.
What's the rationale behind the formula used in the hive_trend_mapper.py program of this Hadoop tutorial on calculating Wikipedia trends?
There are actually two components: a monthly trend and a daily trend. I'm going to focus on the daily trend, but similar questions apply to the monthly one.
In the daily trend, pageviews is an array of number of page views per day for this topic, one element per day, and total_pageviews is the sum of this array:
# pageviews for most recent day
y2 = pageviews[-1]
# pageviews for previous day
y1 = pageviews[-2]
# Simple baseline trend algorithm
slope = y2 - y1
trend = slope * log(1.0 +int(total_pageviews))
error = 1.0/sqrt(int(total_pageviews))
return trend, error
I know what it's doing superficially: it just looks at the change over the past day (slope), and scales this up to the log of 1+total_pageviews (log(1)==0, so this scaling factor is non-negative). It can be seen as treating the month's total pageviews as a weight, but tempered as it grows - this way, the total pageviews stop making a difference for things that are "popular enough," but at the same time big changes on insignificant don't get weighed as much.
But why do this? Why do we want to discount things that were initially unpopular? Shouldn't big deltas matter more for items that have a low constant popularity, and less for items that are already popular (for which the big deltas might fall well within a fraction of a standard deviation)? As a strawman, why not simply take y2-y1 and be done with it?
And what would the error be useful for? The tutorial doesn't really use it meaningfully again. Then again, it doesn't tell us how trend is used either - this is what's plotted in the end product, correct?
Where can I read up for a (preferably introductory) background on the theory here? Is there a name for this madness? Is this a textbook formula somewhere?
Thanks in advance for any answers (or discussion!).
As the in-line comment goes, this is a simple "baseline trend algorithm",
which basically means before you compare the trends of two different pages, you have to establish
a baseline. In many cases, the mean value is used, it's straightforward if you
plot the pageviews against the time axis. This method is widely used in monitoring
water quality, air pollutants, etc. to detect any significant changes w.r.t the baseline.
In OP's case, the slope of pageviews is weighted by the log of totalpageviews.
This sorta uses the totalpageviews as a baseline correction for the slope. As Simon put it, this puts a balance
between two pages with very different totalpageviews.
For exmaple, A has a slope 500 over 1000,000 total pageviews, B is 1000 over 1,000.
A log basically means 1000,000 is ONLY twice more important than 1,000 (rather than 1000 times).
If you only consider the slope, A is less popular than B.
But with a weight, now the measure of popularity of A is the same as B. I think it is quite intuitive:
though A's pageviews is only 500 pageviews, but that's because it's saturating, you still gotta give it enough credit.
As for the error, I believe it comes from the (relative) standard error, which has a factor 1/sqrt(n), where
n is the number of data points. In the code, the error is equal to (1/sqrt(n))*(1/sqrt(mean)).
It roughly translates into : the more data points, the more accurate the trend. I don't see
it is an exact math formula, just a brute trend analysis algorithm, anyway the relative
value is more important in this context.
In summary, I believe it's just an empirical formula. More advanced topics can be found in some biostatistics textbooks (very similar to monitoring the breakout of a flu or the like.)
The code implements statistics (in this case the "baseline trend"), you should educate yourself on that and everything becomes clearer. Wikibooks has a good instroduction.
The algorithm takes into account that new pages are by definition more unpopular than existing ones (because - for example - they are linked from relatively few other places) and suggests that those new pages will grow in popularity over time.
error is the error margin the system expects for its prognoses. The higher error is, the more unlikely the trend will continue as expected.
The reason for moderating the measure by the volume of clicks is not to penalise popular pages but to make sure that you can compare large and small changes with a single measure. If you just use y2 - y1 you will only ever see the click changes on large volume pages. What this is trying to express is "significant" change. 1000 clicks change if you attract 100 clicks is really significant. 1000 click change if you attract 100,000 is less so. What this formula is trying to do is make both of these visible.
Try it out at a few different scales in Excel, you'll get a good view of how it operates.
Hope that helps.
another way to look at it is this:
suppose your page and my page are made at same day, and ur page gets total views about ten million, and mine about 1 million till some point. then suppose the slope at some point is a million for me, and 0.5 million for you. if u just use slope, then i win, but ur page already had more views per day at that point, urs were having 5 million, and mine 1 million, so that a million on mine still makes it 2 million, and urs is 5.5 million for that day. so may be this scaling concept is to try to adjust the results to show that ur page is also good as a trend setter, and its slope is less but it already was more popular, but the scaling is only a log factor, so doesnt seem too problematic to me.