I am having a hard time trying to solve a very easy issue I can imagine, but I just can't see it.
Namely, I am building a dynamic simulation model which calculates accumulated costs and benefits.
However, I have introduced a 5 year time span for the model but there are certain costs and benefits which occur only once (in the first year for example). But currently, the model is using these parameters in each simulation for every year. How can model it such that these values are only taken into the simulation once?
Surely there is some kind of formula which could help me with this. The AnyLogic support page did not help me either.
For applying values that change over time, you should use a "Dynamic Variable" object and set it to 0 after one year as follows (assuming "999" is replaced yb whatever value you want):
Related
I am trying to create a dynamic environment in anylogic with portfolio performance over the time.
The return each year should be dynamic (random) using triangular or normal
Example triangular (-0.5,0.1,0.5) or normal(0.05,0.08)
That means sometimes it is positive and sometimes it's negative
Dynamic variable/parameter is 'Return1'
Flow is 'earn'
Stock is 'portfolio'
I use a variable Return1 = normal(0.05,0.08)
A Flow earn = portfolio*Return1
The stock is called Portfolio and initial value of 100. It accumulates the profit/loss each year
Unfortunately the results in the accumulated portfolio are not correct.
If I use Return1=0.1 or for example -0.05 (fixed prices) it works perfectly.
It seems there are issues in anylogic system dynamics with randomness.
I would appreciate if you can help how I could simulate the portfolio performance in this way
To do what you want, you need to recalculate the random variable in your variable every year. Maybe create an event that runs once per year that changes the value of the variable.
In the event you would have every year:
Return1 = normal(0.05,0.08)
The variable Return1 is not magically updated automatically based on your own desires and wishes, unless you tell Anylogic explicitely.
If return1 is a dynamic variable, then the value will be recalculated every time step, which will lead to unexpected results that will be maybe equivalent to using the average of your random value.
If your time step is 1 year... well in AnyLogic you can't really trust that time step unfortunately.
how to decide how in advance my prediction is?
i am following the featuretools churn tutorial https://github.com/Featuretools/predict-customer-churn
what i don't quite understand how did it decide that the prediction is for one month in advance.. in previous churn examples i tried, i just get aggregated data ( it could be historical for a years or months) then i build churn model and predict but i don't know if my prediction is for a month a year or even how many days in advance how is that decided!.
does it depend on the period of aggregation or the data i didn't use. i know cut off time is the time i want to make prediction but how do i tell the system i want to make prediction for 2 month in advance do i just disregard the data for the last two months by setting the cut_off time but provide the label after the two months and say my model based on the features i get is for a 2 month advanced prediction.
for ex. cut_off date is 1/8/2010 label is the customer state on 1/10/2010
so two months period is the advance prediction? and i used all historical data previous to cut_off time?
this might be a time series problem that is turned into a simple classification but i am not sure!
You pick the amount of time in advanced (called "lead time") using your domain expertise. Depending on the real world application the lead time might be more or less. Sometimes you might even build multiple models with different lead times to apply in different situations.
You control the lead time by moving the cutoff earlier with respect to the time the label became known. So, the example you give looks correct.
How can I determine value from previous time step during simulation in Modelica?
I have equation Q=m*c*(Ts2-Ts1-Tr) I need to extract value of Ts2 and Ts1 from it.
Ts2 - is the value from time step 2
Ts1 - is value from previous time step
Ts is input signal and it has variations during the time. Each step
it has different value. In my case time step is 1s. Other values are
fixed.
Can I set in equation variable time?
For example:
Ts2 (start=time);
Ts (start=time-1);
Or it should be input inside this model?
regards Tymofii
This was addressed in a similar question already.
The key point is that equations describing physical behavior cannot refer to time steps. This is because there is no "timestep" in nature or the laws of physics and so the response of a system cannot depend on it.
You don't really explain why you need to do what you are doing. Are you trying to extract simulation results? Are you trying to correlate to experimental data? Or, are you just trying to solve a differential equation?
It isn't clear what you want to do. Please elaborate and we can probably give you some guidance on how to proceed in Modelica.
Update
Using values from a "previous interval" is fine. For example, if you wanted to sample your solution at regular intervals, express a "z transform" or implement a Kalman Filter in Modelica, you could do each of those very easily (for example, see the 'sample' keyword here). In other words, it is possible to store as many previous values as you would like.
What you cannot do is use the timestep of the continuous solver in expressing how your system behaves. The intervals you reference must be independent from any intervals that the solver is using.
Is it possible to use the previous value of the time varying variable
for eg:
Suppose I have pipe whose inlet temperature is 298K with a specified uniform mass flow(m_flow), now suppose i am heating the pipe using a heater of 100 watts.
The outlet temperature will be attain a higher temperature of suppose 302K, now if i have to use this outlet temperature as my inlet temperature (in the sense i am recircuilating the water), how would i be doing it?
is it possible to update the value of the inlet temperature based on the outlet temperature at the previous timestep? so that for the next iteration the inlet temperature will be the same as the oulet temperature in the previous iteration (in other words the fluid would be recirculating).
Thanks
You cannot access the value in the previous time step. The closest you can get in Modelica is using delay(exp,T) to get the value T units of time ago.
The timestep does not enter into it at all. A model that uses information about timestep is just wrong. Nature doesn't know or care about integration time steps, the model should reflect that.
It seems to me what you want to capture is transport delay. Transport delay is the delay introduced by the time it takes for molecules, electrons, etc. through the system. So presumably what you wish to model is the time it takes the inlet fluid to reach the exit. Again, this has nothing to do with the integration timestep but rather the velocity of the fluid and the distance it must travel. Once you know how long that takes (by either a priori knowledge of the system of by looking at the simulation results themselves), you can follow Marco's suggestion of using the delay operator.
In order to setup a proper model for the system you described I suggest you to look at the example :
Modelica.Thermal.FluidHeatFlow.Examples.IndirectCooling
of the modelica standard library ver. 3.2. Instead of one pipe you can put an ambient or control volume component to better suit you needs. Moreover using continous and differentiable equations (the delay function is not) you will benefit from some of the advantages of the Modelica code, e.g. you will be able to reuse your models in a much wider range of cases, solve inverse problems, solve initial value problems, ...
I hope this helps,
Marco
I have a set of data for the past 5 years. Approx 7000 rows of data with features that are binary {yes/no} or are multi-classed {product A, B, C} A total of about 20+ features.
I am trying to make a program (or one time analysis project) to determine (predict) the product shipdate(shipping delay days) based on this historical data. I have 2 columns that indicate when a product was planned to be shipped and another column of when it was actually shipped! Currently.
I'm wondering how I can make a prediction program that determines based on the historic data when new data input of a product will expect to ship. I don't care about a getting a specific date but even just a program that can tell me number of delay days to add...
I took an ML class a while back and I wasn't sure how to start something like this. Any advice? Plus the closest thing to this I can think of is an image recognition assignment using NN. but that was too easy here I have to deal with a date instead of pixel white/black.... I used Matlab back in the day (I still know how to use it) but I just downloaded Weka data mining tool.
I was thinking of a neural network but I'm not sure how to set it up to have my program give me a the expected delay time (# of days/month) from the inputed ship date.
Basically,
I want to input (size = 5, prod = A, ....,expected ship date = jan 1st)
and the program returns the number of days to add as a delay onto my expected ship date given the historical trends...
Would appreciate any any help on how start something like this the correct/easiest/best way... Thanks in advance.
If you use weka, then get your input/label data into the arff format and then you try out all the different regressors (this is a regression problem after all). To avoid having to do too much programming quite yet (if you are just in an exploratory phase), use the weka experimenter which has a GUI for trying out a whole bunch of regressors on your dataset.
Then when you find one that does something expected and you want to do some more data analysis using MATLAB, then you can use a weka/matlab interface.