Is there a function or library (like auto_arima to get the order(p,d,q) values) available to calculate the P, D, Q and M values to be used in Seasional_order(P,D,Q,M) in SARIMAX model.
Thanks,
The auto_arima function can do that. You can set the parameter seasonal = True and give the length of the season with the parameter m:
auto_arima(y=your_data, seasonal=True, m=length)
If you want to only use the seasonal components without the non-seasonals, then you can manually turn them off by setting the respective parameters to 0:
auto_arima(y=your_data, seasonal=True, m=length, p=0, d=0, q=0)
However, auto_arima cannot really detect whether your data is stationary and therefore, you need to estimate the d and D parameters yourself and manually set them in the auto_arima function.
https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.auto_arima.html
Related
How long will it take for a program to print a truth table for n propositional symbols?
(Symbols: P1, P2, ..., Pn)
Can't seem to crack this question, not quite sure how to calculate this instance.
It will take time proportional to at least n*2^n. Each of the propositional symbols can assume one of two values - true or false. The portion of the truth table that lists all possible assignments of n such variables must have at least 2 * 2 * … * 2 (n times) = 2^n rows of n entries each; and that's not even counting the subexpressions that make up the rest of the table. This lower bound is tight since we can imagine the proposition P1 and P2 and … and Pn and the following procedure taking time Theta(n*2^n) to write out the answer:
fill up P1's column with 2^(n-1) TRUE and then 2^(n-1) FALSE
fill up P2's column with 2^(n-2) TRUE and then 2^(n-2) FALSE, alternating
…
fill up Pn's column with 1 TRUE and 1 FALSE, alternating
fill up the last column with a TRUE at the top and FALSE all the way down
If you have more complicated propositions then you should probably take the number of subexpressions as another independent variable since that could have an asymptotically relevant effect (using n propositional symbols, you can have arbitrarily many unique subexpressions that must be given their own columns in a complete truth table).
I have data consisting of n key-value pairs in a table, A
I'd like to produce another table, B of size (n+1) x (n+1), where the first row/column are the keys of the original table, and entry (i,j) is some function of the ith and jth value
Ex:
A:
K|V
---
a 1
b 2
c 3
B:
a b c
a f(1,1) f(1,2) f(1,3)
b f(2,1) f(2,2) f(2,3)
c f(3,1) f(3,2) f(3,3)
Depends on the function you need. Assuming B2:B4 contains {1,2,3} , The following can be done. Each will use different functions and will add or subtract in different ways. The first one is the only one, that'll work in the exact way you asked, but that's just for Matrix multiplication - You could maybe use that as base and do other functions on it as needed.
=ARRAYFORMULA(MMULT(--(B2:B4), --TRANSPOSE(B2:B4)))
=ARRAYFORMULA((--(B2:B4)/--TRANSPOSE(B2:B4)))
=ARRAYFORMULA((--(B2:B4)+--TRANSPOSE(B2:B4)))
=ARRAYFORMULA((--(B2:B4)---TRANSPOSE(B2:B4)))
In h2o.randomForest, lets say I have 5 input features x=c("A","B","C","D","E"), is there anyway to force the algorithm to always choose A,B AND one of the remaining features?
In this case h2o.randomForest is just asking you to pass correct x (list of columns to use in prediction) and y (the column name to do prediction) so anything you will pass will be used as input.
What you are asking is a python specific question. How you want to pass the list of columns you will need to write logic for it. You can defined the following is a function and use it as needed.
import random
myframe = ["a","b","c","d","e"]
//You can also set myframe as column name list
//myframe.remove(_use_response_column_name) this will make it generic
selectedkeys = ["a","b"]
for item in selectedkeys:
if item in myframe:
myframe.remove(item)
selectedkeys.append(random.choice(myframe))
print(selectedkeys)
print(myframe)
You just need to pass the selectedkeys as input for X.
In SPSS i have a variabele with a lot of different values (8 figure number; 00000000). Every row is a person. I want to aggregate this data on postal area and count the number of different values in a postal area. Is there a way?
Result within a postal area should be 1 to N : 1 = every person has the same value, N = every person has a different value
Aggregate in two steps. Assuming your dataset name is data1, with variables var1 (the variable of interest) and postalcode, I would do this:
Create a dataset step1, with one row for each combination of values of postalcode and var1. Also possible by using the command casestovars.
dataset declare step1.
dataset activate data1.
aggregate outf=step1 /break=postalcode var1 /n=n(var1).
Create a dataset result with one row for each postalcode, and a variable n for the number of rows from the previous dataset step1.
dataset declare result.
dataset activate step1.
aggregate outf=result /break=postalcode /n=n(var1).
So, in conclusion: first break by both of the variables, then break only by the variable of postal code. This should do the trick!
Stata has r() macro for values that some commands return (return list after the command).
I need similar access to x after list x if y == 1, but list returns only r(N), not values themselves.
Is it possible to get the observations as a local or global macro to refer to it in the code?
Try levelsof command to get distinct values. It's the cat's pajamas.
One way to save values of all observations (i.e. including repeated) is with a loop:
clear
set more off
*----- exmple data -----
sysuse auto
keep rep78
list
*----- what you want -----
forvalues i = 1/`=_N' {
local myvals `myvals' `=rep78[`i']'
}
display "`myvals'"
But more importantly, why do you think you need such a thing?