How to implement ARX(AutoRegressive with Exogenous) model for time series data using wavelet transform in R? [closed]

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Closed 9 years ago.

There are time series data on which I want to build a ARX model.The data is like:

Time             Volume
00:00hr          4632131
01:00hr          4564653
02:00hr          6313986
.......          .......
.......          .......
23:00hr          7986456

Can anyone help me in solving the above problem. The exogenous input for the above time series are:

-644691181
-121187080
353422690
417492115
-504192375
420646272
-47480551
260350503
2151074145
1251550732
788874753
540183268
396739715
948170766
-1433091907
-148444555
-840182654
-893652578
-1738734435
-1431476210
24974246
93873803
-324033231
479813749

Answer 1

I use fastVAR package. I am not a time series proficient, so I can't be sure from the rsult.

exos <- c(-644691181, -121187080, 353422690, 417492115, -504192375, 420646272, 
  -47480551, 260350503, 2151074145, 1251550732, 788874753, 540183268, 
  396739715, 948170766, -1433091907, -148444555, -840182654, -893652578, 
  -1738734435, -1431476210, 24974246, 93873803, -324033231, 479813749
)

dat <- sample(4632131:100000,24 )
library(fastVAR)
fastVARX(matrix(dat),matrix(exos),3,2,getdiag=FALSE)
Call:
lm(formula = varxz$y.p ~ varxz$Z)

Coefficients:
(Intercept)   varxz$Z.l1   varxz$Z.l2   varxz$Z.l3   varxz$Z.l1   varxz$Z.l2  
  4.642e+06   -2.182e-01   -2.607e-01   -4.587e-01    2.963e-04   -3.743e-04