I know there must be a simple way that I can learn to do this but I cannot imagine how to start. I am tasked with finding a top 10 matching daily wind power time series in a 30-day plus/minus window from the first day in the time series (Jan 1st) matching a single daily wind power time series and it is out of my level of experience in MATLAB. I have successfully done this matching a single time series of the current year with the exact calendar days from previous years, but I need a more robust searching method to find the best correlated time series in a +/- window of time. For example, I'm comparing a 120 day time series (without leap years) with 25 previous years during the same 120-day period (Jan-Apr). The end result will show me the top 10 time series with the years and Julian day or cumulative day listed and a correlation or RMSE value associated with it. My data looks like this arranged in a 365 (days) X 25 (years) array and I thank you very much for your help!
1182573 470528 1638232 2105034 1070466 478257 1096999
879997 715531 1111498 1004556 1894202 1372178 1707984
636173 937769 2119436 742710 1625931 1275567 1228515
967360 1103082 2218855 1643898 1822868 554769 1325642
Related
I am working on a system dynamics model, whose units are days, in AnyLogic. The model tracks daily demand of water for 10,950 days (30 years). One of the model’s outputs is a timeplot that keeps track of this demand, but I don’t want it to plot the daily demand. Instead, I want the timeplot to show demand in years (i.e. the sum of 365 days across the 30 years). I am having a bit of trouble finding a way to do this and would appreciate any help. Thank you!
I assume your problem is twofold.
How to get the time plot to display 30 years of data
How to sum the annual demand for 30 years
Here is a simple example that I believe answers your question.
In this simple model, there is a daily event that simulates the daily demand, and adds it to a variable called annualDemand
There is another event that runs yearly and tasks the annualDemand and saves it to a data set, and rests the annual demand accumulator to 0.
In your time plot, you simply display the dataset which will at the end of the model only contain 30 entries, one for each year.
By following the same principles
Save annual demand
yearly event to add annual demand to data set and reset the annual demand
time plot to plot the dataset
You should be able to get what you need.
enter image description hereI am hoping that someone can help me with the below problem.
I am trying to create a field in a database to indicate the date that a trainee will move to thier next year of training. They go to next year of training after 52 weeks training. They may have multiple placements during a particular training year.
I have their training start date (trainee::startdate... their start and end date (traineeplacement::startdate and traineeplacement::enddate) of each job (in a linked table), their percentage of full time (traineeplacement::PercentageFullTime) and the calculated number of weeks whole time equilavent in the job (traineeplacement::durationweeks).
Through using the cumulative time in a training (traineeplacement::training-duration), in excel I can find the end of the block before using Max statment with an imbedded if statement to find max value less than 52 weeks in cumulative time in training. I cannot seem to do this in filemaker. I would then like to find the next line in the traineeplacement table and from that I can calculate the end of training year date from start of placment date, % of fulltime, and number of weeks neediing to be worked to get to 52 weeks...
The placements are measured in weeks for ease and training runs over a minimum of 4 years, calculated from start and end dates of each placement. The placement dates are a consecutive, but I will only count the product of weeks worked and % of full time (full time =100%, not working (e.g. maternity leave) = 0%). Hope this makes sense and I have included a screen shot of the draft of the database to give you an idea of what I mean.
I hope this makes sense.
enter image description here
Excel spreadsheet
Excel Spreadsheet Formulae
I'm having problems creating a graph of the average number of people inside a 24h shopping complex. I have two columns of data on a spreadsheet of the times a customer comes in (intime) and when he leaves (outtime). The data spans a couple of years and is in datetime format (dd-mm-yyyy hh:mm:ss).
I want to make a graph of the data with time of day as x-axis, and average number of people as y-axis. So the graph would display the average number of people inside during the day.
Problems arise because the place is open 24h and the timespan of data is years. Also customer intime & outtime might be on different days.
Example:
intime 2.1.2017 21:50
outtime 3.1.2017 8:31
Any idea how to display the data easily using Matlab?
Been on this for multiple hours without any progress...
Seems like you need to decide what defines a customer being in the shop during the day, is 1 min enough? is there a minimum time length under which you don't want to count it as a visit?
In the former case you shouldn't be concerned with the hours at all, and just count it as 1 entry if the entry and exit are in the same day or as 2 different entries if not.
It's been a couple of years since I coded actively in matlab and I don't have a handy IDE but if you add the code you got so far, I can fix it for you.
I think you need to start by just plotting the raw count of people in the complex at the given times. Once that is visualized it may help you determine how you want to define "average people per day" and how to go about calculating it. Does that mean average at a given time or total "ins" per day? Ex. 100 people enter the complex in a day ... but on average there are only 5 in the complex at a given time. Which stat is more important? Maybe you want both.
Here is an example of how to get the raw plot of # of people at any given time. I simulated your in & out time with random numbers.
inTime = cumsum(rand(100,1)); %They show up randomly
outTime = inTime + rand(100,1) + 0.25; % Stay for 0.25 to 1.25 hrs
inCount = ones(size(inTime)); %Add one for each entry
outCount = ones(size(outTime))*-1; %Subtract one for each exit.
allTime = [inTime; outTime]; %Stick them together.
allCount = [inCount; outCount];
[allTime, idx] = sort(allTime);%Sort the timestamps
allCount = allCount(idx); %Sort counts by the timestamps
allCount = cumsum(allCount); %total at any given time.
plot(allTime,allCount);%total at any given time.
Note that the x-values are not uniformly spaced.
IF you decide are more interested in total customers per day then you could just find the intTimes with in a given time range (each day) & probably just ignore the outTimes all together.
I'm in healthcare and we're trying to assess the number of discharges we have per hour of day, but we'd also like to be able to filter them down by day of week, or specific month, or even a particular day of week in a particular month (e.g. " what is the average number of discharges per hour on Mondays in January?")
I'm confident that Tableau can do this, but haven't been able to make the averages show up in my line graph... every time that I convert it from COUNT to AVG, the line simply goes straight. I got close when I did a table calculation to find the Average (dividing the count per hour by the number of days captured in the report), but when I add a filter for either the month or day of week, selecting one of the options of the filter reduces the total number that is being counted, rather than re-averaging the non-filtered items. (i.e. if the average of the 7 days of the week is "10" for a particular hour, and I deselect the first three days of the week, it's now saying that my average for that hour is roughly 6, despite the fact that all of the days are very close to 10 at that hour.)
Currently, my data table has the following columns:
Account#/MonthYear/HourOfDay/DayOfWeek
ex.12345678/ Jan-17 / 12 /Sunday
I would just create a few calculated fields to differentiate the parts of the calendar you might want to filter/aggregate on. Mixing the month and day of the week with filtering is pretty straight forward with the calculated fields. Then do standard summing to get what you are looking for because an average count of records is always one unless you are throwing some other calculation into the mix. I threw a quick example up on Tableau Public for you to get the idea.
Setting
As I'm sure many of you do in your vizs, I use date parameters for my data. This is great for creating trend analyses and all types of time series representations. Currently I'm using a line graph to show our sales hit rate history.
Picture
Question
The problem I'm running into is in creating a four week moving average. As you can see the four week moving average doesn't become just that until four weeks in! This creates quite the problem for me. What methods will enable the average at t=0 to show the average for the preceding four weeks?
Formula Used
This is my formula for creating the four week moving average:
WINDOW_AVG([Hit Ratio],-27,0)
Remove your date filter and try:
IIF(ATTR([DATE_FIELD])<T=0,NULL,WINDOW_AVG([Hit Ratio],-27,0))