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Need urgent help regarding this.
I am currently working on a project involving multiple time series forecasting using R. For the forecasting process, I have monthly data available from 2019 up to 2023.
Currently, I have generated baseline forecasts using R, specifically using ARIMA and ETS models. I have attached figures depicting the behavior of one of the time series along with its corresponding forecasted values based on the applied mathematical models.
However, I am facing an issue where most of the forecasted data does not seem to capture the seasonality well. The forecasts appear to be linear rather than capturing the expected seasonal patterns.
However, I am facing an issue where most of the forecasted data does not seem to capture the seasonality well. The forecasts appear to be linear rather than capturing the expected seasonal patterns.
Code:
t_data.fit = t_data %>%
model(arima=ARIMA(Demand)
)
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There are many reasons why your forecasts may appear to be linear rather than capturing the expected seasonal patterns. One possible reason is that the model you are using is not appropriate for the data you are working with. Another possible reason is that you have not included all of the relevant variables in your model.
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I have been assigned the task of performing business sales forecasting using time series analysis. However, before starting the forecasting process, I need to identify and treat the outliers in the dataset.
To achieve this, I have decided to use Seasonal Trend Decomposition (STL) with LOESS.
I would appreciate your assistance in implementing this technique using Python or R programming language.
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Take at the attached. Best wishes David Booth
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A collection of solved examples in Pyomo environment (Python package)
The solved problems are mainly related to supply chain management and power systems.
Feel free to follow / branch / contribute
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udemy. com/course/optimization-in-python/?couponCode= 36C6F6B228A087695AD9
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Hello,
I'm undergraduate student in my final year. I'm stuck with my thesis on how to get pareto formula because in journal only have the final function not how to get it. I used Journal from EMEL KIZILOK KARA (A Study on Modeling of Lifetime with Right-Truncated Composite LognormalPareto Distribution: Actuarial Premium Calculations). Attached are my work so far. Sorry if my english is bad. Please help me to solve this.
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you can read this paper, I hope it will help you.
Deng, Min, Mostafa Aminzadeh, and Min Ji. 2021. Bayesian Predictive Analysis of Natural Disaster Losses. Risks 9: 12. https://doi.org/10.3390/risks9010012
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I am working on portfolio optimization using lower partial moment of order 1, can someone help me how to implement LPM-1 in excel sheet using "tau" as my threshold value as 0.00% and order (n) as 1.
Thank you all in advance for your contributions to my question.
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How to find the distance from C to S in this 3D Ellipsoid .And the how to get the distance D between two foci ? Let say coordiantes a=5, b=4, c=4.
Question 3. The Distance between -C to S and between S to C must be equal to 2a. Can anyone solve it with example please?
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I assume that a,b,c are the lengths of semi-axis. The distance CS=a=5.
In the first example where b=c is this elipsoid a solid of revolution, thus the foci of the ellipse are also foci of the solid.
Given a=5, b=4 we can compute the focal distance with the use of Pythagorean theorem: (D/2)2=52-42=9. Thus D/2=3 and D=6.
If a=5, b=4, c=3 is the solid different, but the situation in the plane (F1F2S) is as above. D=6.
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I have a function as follows:
y= a*x^b
a=7e-5
b=-0.755
I attached a simple graph of the function. As it is apparent from the graph the CURVATURE of the function increases from ZERO to a finite value (around x=0.1-0.2) and then it decreases to reach a value of ZERO. I did my best to draw the CURVATURE of this function using the following formula:
K=f"/(1+f'^2)^1.5
However, using this formula I could not reach the predictable trend of the curvature. Do you have any idea what is the problem?
I can work with MATLAB and Excel.
Your help is appreciated in advance.
Kind regards,
Ebrahim
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Check the power index in the denominator of your expression for the curvature. It should be 3/2 rather than 1/2. Good luck
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Project management in research project
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Yes, it is one of the foundations of research
Best Regards Gh. Javan-Doloei
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Do you think that the iThenticate/CrossCheck/Similarity Index would cause heavy and serious confusion in mathematics? Even destroy, ruin, damage Mathematics? Our mathematics and mathematicians should follow and inherite symbols, phrases, terminology, notions, notations in previous papers, but now we have to change these to avoid, to escape, to hide, to decrease the iThenticate/CrossCheck/Similarity Index! It’s very ridiculous for mathematics and mathematicians! Mathematics is disappearing! being damaged!
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Yes! Even standard mathematical symbols and notations are captured in similarity index. The habit of using unconventional symbols and notations just to reduce similarity index is destroying the beauty and taste of mathematics.
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After estimating an ARDL model in eviews, you can ask for the long run coefficients by clicking view > coefficients diagnostic > long run form and bound test. My question is: how standard errors of the long run coefficients are calculated? I know they are related to "Delta method" and I tried to search more about it and how standard errors of the long run coefficients are calculated in eviews user guide, but I couldn't.
I would appreciate any guide in how to calculate standard errors of the long coefficients.
Thanks & regards.
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see for instance on the internet the stata explanation: http://www.stata.com/support/faqs/statistics/delta-method
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In linear stability analysis of double diffusive convection can anyone  please tell that how perturbation equations (1) in the link is achieved. I am confused. 
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Hi Faria,
I am assuming that you are happy with what the full perturbation equationss are, from which those equations (1) have been derived. I am also assuming that you need to know why (1) is correct for tall salt fingers.
The derivation follows directly from the assumption that z is very large compared with x and y for the salt fingers. Mathematically, if z >> x, then d/dz << d/dx. Physically this corresponds to saying that the distance over which variations take place in the x-direction is much smaller than the distance in the z-direction, and therefore x-derivatives are much large than z-derivatives. If one imagines a single convection cell in a very tall cavity, then hopefully (!) it will be clear.
Therefore w_xx+w_yy+w_zz is well-approximated by w_xx+w_yy because w_zz is negligible.
Essentially this is an order-of-magnitude analysis of the type that one uses in boundary layer theory.
Cheers,
Andrew
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Hi RGaters,
Does anyone know how to modify the order in a sample to modify Kendall's tau value(s) ? Let me clarify what I am looking for. Consider that we have N realizations of K random variables. Each realizations of the group of K variables is independent from the other ones. Inside this realization, the K variables might be independent or not, we do not really care. They even can follow different distributions. The question is the following : from any sample (size N x K) that we call M, can I exchange the places of M[i_1,1] with M[i_2,1], M[i_3,2] with M[i_4,2] and so on, possibly coming back to the first column with an interative algorithm to finally obtain as a result a new (rearranged) sample M' where tau[1,2] = first wanted value, tau[1,3] = second wanted value, and so on, i.e. can I get rearranged data to get a desired tau-matrix. If yes, any algorithm to suggest ?
I used Cholesky method to do the same thing to reach a desired Pearson-correlation matrix, but I have to admit I'm facing a wall on this issue right now.
Any help is welcome !
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Hi René,
Thanks for your answer, however, I work in practice with 100 000 * 10 (it's true that I did not mentioned this above) matrices that I want to rearrange this sample to reach a target kendall's tau matrix (as close as possible). It is not the kendall's tau matrix that I want to rearrange, it is the data which lead to this kendall's tau matrix. That'd be too easy :)
Considering this, there is absolutely no way that I try every possible combinations and see which one to choose. I need some intelligent trick such as Cholesky method to get correlate variables.
So basically, it's not only about programming skills, it's also about applied mathematics.
Best regards !
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What are the latest research on bath tub shaped failure rate functions?
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Sure,
its a standard survival analysis text, most of the focus is on standard non/semi parametric approaches (ie Kaplan-Meier / Cox models), but early on there is a good discussion around parametric models and model fitting as I recall. 
Survival Analysis: Techniques for Censored and Truncated Data (Statistics for Biology and Health) Hardcover – March 10, 2005
by John P. Klein (Author), Melvin L. Moeschberger (Author)
ISBN-13: 978-0387953991 ISBN-10: 038795399X Edition: 2nd
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It is well know that each Borel measure m on metric space is regular, i.e. for Borel set A and any d>0 there are open set G and close set F, such that F\subset A\subset G and m(G\F)<d. Is there constructive proof of this fact in the sense that using set A we can build up the sets F and G? Is there procedure of building F and G?
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This is a good question.
Regularity is important in the description of multiscale measures and functions.  This is central theme in
M.L Lapidus, J.A. Rock, Toward zeta functions and complex dimensions of multifractals, arXiv 0810, 2008, no. 0789v1,  1-16,  downloadable from RG.
The regularity of a Borel measure is defined on page 3 (see Def. 2.1).   Partition zeta functions are introduced in Section 4, starting on page 8, defined for any Borel measure.   See Theorem 5.2, starting on page 12, the regularity of a Borel measure is considered.
Reverse mathematics is considered in proving the measure-theoretic regularity in the Borel hierarchy in
S.G. Simpson, Mass problems and measure-theoretic regularity, Penn. State University, 2014:
See Section 7, starting on page 20.   There are lots of interesting results in this section.
It is proved that the outer regularity of locally finite Borel measures in metric spaces in 
J.T. Tyson, Metric and geometric quasiconformality in Ahlfors regular Loewner spaces,  Conformal Geometry and Dynamics 5, 2001, 21-73:
See Proposition 1.4, starting on page 27.
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At the moment I am trying to judge the validity of a model assuming a CARA-Utility-Function. And I vaguely remember that experimental results usually suggest decreasing absolute risk aversion.
Unfortunately I cannot find any literature on estimations of risk-aversion and its behavior when stakes get higher at all.
Are there papers like this?
Thanks for your help!
Michael
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Let me correct your question to the following one: how could I apprioximate a nonlinear term by a linear one? Detailed answer must be based on formulation of your problem.
1) For instance, if your problem concerns a nonlinear partial differential equation (NPDE) of the following form (H+eV)f=r, where H is linear operator, V is your non-linear term, e is perturbation small parameter and f is your target then you can apply perturbation method.
2) There are nonlinear problems which can be solved exactly. For instance there is class 1+1 dimensional of NPDE which can be handled by Inverse scattering method, or by the Backlund transformation. Et cetera. Display more about your problem.
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Solve for B, t1, t2
A1 =B.e^pt1
A2=B.e^pt2 .
t2 - t1 = c ; known constant.
p also another known constant.
Any method is acceptable.
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If A1=A2/(e^p.c) then the system has infinity number of solution, as
B=u, t1=ln(A1/u)/p, t2=t1+c.
If A1<>A2/(e^p.c) then the system has not a soluton.