fortsa_model
fortsa_model contains useful time series models.
!> `Fortran-TSA-api.f90` will be the `C-Fortran` interface to [rafat/CTSA](https://github.com/rafat/ctsa)
module fortsa_model_m
use, intrinsic :: iso_c_binding, only: c_int, c_double, c_char, c_ptr, c_null_ptr, c_loc
implicit none
private
public :: arima_set, arima_init, arima_setMethod, arima_setOptMethod, &
arima_exec, arima_summary, arima_predict, arima_free
public :: ar_init, ar_exec, &
ar_summary, ar_predict, &
ar_free
public :: auto_arima_init, auto_arima_setApproximation, auto_arima_exec, &
auto_arima_summary, auto_arima_predict, &
auto_arima_free, auto_arima_setStepwise, auto_arima_setVerbose
public :: sarima_init, sarima_predict, sarima_setMethod, sarima_setOptMethod, &
sarima_vcov, sarima_exec, sarima_free, sarima_summary
public :: sarimax_init, sarimax_setMethod, sarimax_exec, &
sarimax_summary, sarimax_predict, sarimax_free
public :: yw, burg, hr
public :: auto_arima_set
type, bind(c) :: auto_arima_set
integer(c_int) :: N !! length of time series
integer(c_int) :: Nused !! length of time series after differencing, Nused = N - d
integer(c_int) :: method
integer(c_int) :: optmethod
integer(c_int) :: pmax !! Maximum size of phi
integer(c_int) :: dmax !! Maximum Number of times the series is to be differenced
integer(c_int) :: qmax !! Maximum size of theta
integer(c_int) :: pmax_ !! Maximum Size of seasonal phi
integer(c_int) :: dmax_ !! Maximum number of times the seasonal series is to be differenced
integer(c_int) :: qmax_ !! Maximum size of Seasonal Theta
integer(c_int) :: p !! size of phi
integer(c_int) :: d !! Number of times the series is to be differenced
integer(c_int) :: q !! size of theta
integer(c_int) :: s !! Seasonality/Period
integer(c_int) :: p_ !! Size of seasonal phi
integer(c_int) :: d_ !! The number of times the seasonal series is to be differenced
integer(c_int) :: q_ !! size of Seasonal Theta
integer(c_int) :: r !! Number of exogenous variables
integer(c_int) :: M !! M = 0 if mean is 0.0 else M = 1
integer(c_int) :: ncoeff !! Total Number of Coefficients to be estimated
real(c_double) :: phi
real(c_double) :: theta
real(c_double) :: phi_
real(c_double) :: theta_
real(c_double) :: exog
real(c_double) :: vcov !! Variance-Covariance Matrix Of length lvcov
integer(c_int) :: lvcov !! length of VCOV
real(c_double) :: res
real(c_double) :: mean
real(c_double) :: var
real(c_double) :: loglik
real(c_double) :: ic
integer(c_int) :: retval
integer(c_int) :: start
integer(c_int) :: imean
integer(c_int) :: idrift
integer(c_int) :: stationary
integer(c_int) :: seasonal
integer(c_int) :: Order_max
integer(c_int) :: p_start
integer(c_int) :: q_start
integer(c_int) :: P_start_
integer(c_int) :: Q_start_
character(c_char), dimension(10) :: information_criteria
integer(c_int) :: stepwise
integer(c_int) :: num_models
integer(c_int) :: approximation
integer(c_int) :: verbose
character(c_char), dimension(10) :: test
character(c_char), dimension(10) :: type
character(c_char), dimension(10) :: seas
real(c_double) :: alpha_test
real(c_double) :: alpha_seas
real(c_double) :: lambda
real(c_double) :: sigma2
real(c_double) :: aic
real(c_double) :: bic
real(c_double) :: aicc
real(c_double), dimension(0) :: params
end type auto_arima_set
interface
function auto_arima_init(pdqmax, pdqmax_, s, r, N) bind(c, name='auto_arima_init')
import c_int, c_ptr
integer(c_int) :: pdqmax(*), pdqmax_(*)
integer(c_int), value :: s, r, N
type(c_ptr) :: auto_arima_init
end function auto_arima_init
end interface
type, bind(c) :: sarimax_set
integer(c_int) :: N !! length of time series
integer(c_int) :: Nused !!length of time series after differencing, Nused = N - d
integer(c_int) :: method
integer(c_int) :: optmethod
integer(c_int) :: p !! size of phi
integer(c_int) :: d !! Number of times the series is to be differenced
integer(c_int) :: q !!size of theta
integer(c_int) :: s !! Seasonality/Period
integer(c_int) :: p_ !!Size of seasonal phi
integer(c_int) :: d_ !! The number of times the seasonal series is to be differenced
integer(c_int) :: q_ !!size of Seasonal Theta
integer(c_int) :: r !! Number of exogenous variables
integer(c_int) :: M !! M = 0 if mean is 0.0 else M = 1
integer(c_int) :: ncoeff !! Total Number of Coefficients to be estimated
real(c_double) :: phi
real(c_double) :: theta
real(c_double) :: phi_
real(c_double) :: theta_
real(c_double) :: exog
real(c_double) :: vcov !! Variance-Covariance Matrix Of length lvcov
integer(c_int) :: lvcov !!length of VCOV
real(c_double) :: res
real(c_double) :: mean
real(c_double) :: var
real(c_double) :: loglik
real(c_double) :: aic
integer(c_int) :: retval
integer(c_int) :: start
integer(c_int) :: imean
real(c_double), dimension(0) :: params
end type sarimax_set
interface
function sarimax_init(p, d, q, p_, d_, q_, s, r, imean, N) bind(c, name='sarimax_init')
import c_int, c_ptr
integer(c_int), value :: p, d, q, p_, d_, q_, s, r, imean, N
type(c_ptr) :: sarimax_init
!! `sarimax_set` struct
end function sarimax_init
end interface
type, bind(c) :: arima_set
integer(c_int) :: N !! length of time series
integer(c_int) :: Nused !! length of time series after differencing, Nused = N - d
integer(c_int) :: method
integer(c_int) :: optmethod
integer(c_int) :: p !! size of phi
integer(c_int) :: d !! Number of times the series is to be differenced
integer(c_int) :: q !! size of theta
integer(c_int) :: s !! Seasonality/Period
integer(c_int) :: p_ !! Size of seasonal phi
integer(c_int) :: d_ !! The number of times the seasonal series is to be differenced
integer(c_int) :: q_ !! size of Seasonal Theta
integer(c_int) :: r !! Number of exogenous variables
integer(c_int) :: M !! M = 0 if mean is 0.0 else M = 1
integer(c_int) :: ncoeff !! Total Number of Coefficients to be estimated
type(c_ptr) :: phi
type(c_ptr) :: theta
type(c_ptr) :: phi_
type(c_ptr) :: theta_
type(c_ptr) :: exog
type(c_ptr) :: vcov !! Variance-Covariance Matrix Of length lvcov
integer(c_int) :: lvcov !! length of VCOV
type(c_ptr) :: res
real(c_double) :: mean
real(c_double) :: var
real(c_double) :: loglik
real(c_double) :: aic
integer(c_int) :: retval
integer(c_int) :: start
integer(c_int) :: imean
real(c_double), dimension(0) :: params
end type
! type(c_ptr), bind(c, name='arima_object') :: arima_object
interface
function arima_init(p, d, q, N) bind(c, name='arima_init')
import c_int, c_ptr
integer(c_int), value :: p, d, q, N
type(c_ptr) :: arima_init
end function
end interface
type, bind(c) :: sarima_set
integer(c_int) :: N !! length of time series
integer(c_int) :: Nused !! length of time series after differencing, Nused = N - d - s*D
integer(c_int) :: method
integer(c_int) :: optmethod
integer(c_int) :: p !! size of phi
integer(c_int) :: d !! Number of times the series is to be differenced
integer(c_int) :: q !! size of theta
integer(c_int) :: s !! Seasonality/Period
integer(c_int) :: P_ !! Size of seasonal phi
integer(c_int) :: D_ !! The number of times the seasonal series is to be differenced
integer(c_int) :: Q_ !! size of Seasonal Theta
integer(c_int) :: M !! M = 0 if mean is 0.0 else M = 1
integer(c_int) :: ncoeff !! Total Number of Coefficients to be estimated
real(c_double) :: phi
real(c_double) :: theta
real(c_double) :: PHI_
real(c_double) :: THETA_
real(c_double) :: vcov !! Variance-Covariance Matrix Of length lvcov
integer(c_int) :: lvcov !! length of VCOV
real(c_double) :: res
real(c_double) :: mean
real(c_double) :: var
real(c_double) :: loglik
real(c_double) :: aic
integer(c_int) :: retval
real(c_double), dimension(0) :: params
end type
interface
function sarima_init(p, d, q, s, p_, d_, q_, N) bind(c, name='sarima_init')
import c_int, c_ptr
integer(c_int), value :: p, d, q, s, p_, d_, q_, N
type(c_ptr) :: sarima_init
end function
end interface
type, bind(c) :: ar_set
integer(c_int) :: N !! length of time series
integer(c_int) :: method
integer(c_int) :: optmethod !! Valid only for MLE estimation
integer(c_int) :: p !! size of phi
integer(c_int) :: order !! order = p
integer(c_int) :: ordermax !! Set Maximum order to be fit
real(c_double) :: phi
real(c_double) :: res
real(c_double) :: mean
real(c_double) :: var
real(c_double) :: aic
integer(c_int) :: retval
real(c_double), dimension(0) :: params
end type
interface
function ar_init(method, N) bind(c, name='ar_init')
import c_int, c_ptr
integer(c_int), value :: method, N
type(c_ptr) :: ar_init
end function
end interface
interface
! exec routines 🔻
subroutine ctsa_sarimax_exec(obj, inp, xreg) bind(c, name='sarimax_exec')
import c_ptr, c_double
type(c_ptr), value :: obj
real(c_double) :: inp(*)
type(c_ptr), value :: xreg
end subroutine ctsa_sarimax_exec
subroutine arima_exec(obj, x) bind(c, name='arima_exec')
import c_ptr, c_double
type(c_ptr), value :: obj
real(c_double) :: x(*)
end subroutine arima_exec
subroutine sarima_exec(obj, x) bind(c, name='sarima_exec')
import c_double, c_ptr
type(c_ptr), value :: obj
real(c_double) :: x(*)
end subroutine sarima_exec
subroutine ctsa_auto_arima_exec(obj, inp, xreg) bind(c, name='auto_arima_exec')
import c_double, c_ptr
type(c_ptr), value :: obj
real(c_double) :: inp(*)
type(c_ptr), value :: xreg
end subroutine ctsa_auto_arima_exec
subroutine ar_exec(obj, inp) bind(c, name='ar_exec')
import c_double, c_ptr
type(c_ptr), value :: obj
real(c_double) :: inp(*)
end subroutine ar_exec
! predict routines 🔻
subroutine arima_predict(obj, inp, L, xpred, amse) bind(c, name='arima_predict')
import c_int, c_ptr, c_double
type(c_ptr), value :: obj
real(c_double) :: inp(*), xpred(*), amse(*)
integer(c_int), value :: L
end subroutine arima_predict
subroutine sarima_predict(obj, inp, L, xpred, amse) bind(c, name='sarima_predict')
import c_int, c_double, c_ptr
type(c_ptr), value :: obj
real(c_double) :: inp(*), xpred(*), amse(*)
integer(c_int), value :: L
end subroutine sarima_predict
subroutine sarimax_predict(obj, inp, xreg, L, newxreg, xpred, amse) bind(c, name='sarimax_predict')
import c_ptr, c_int, c_double
type(c_ptr), value :: obj
real(c_double) :: inp(*), xreg(*), newxreg(*), xpred(*), amse(*)
integer(c_int), value :: L
end subroutine sarimax_predict
!> Predictes the next L values of the time series
subroutine auto_arima_predict(obj, inp, xreg, L, newxreg, xpred, amse) bind(c)
import c_int, c_ptr, c_double
!> Pointer points to `auto_arima_object`
type(c_ptr), value :: obj
!> Input array
real(c_double) :: inp(*)
!> TODO:
real(c_double) :: xreg(*), newxreg(*)
!> Prediction array
real(c_double) :: xpred(*)
!> Standard errors array
real(c_double) :: amse(*)
!> Length of prediction
integer(c_int), value :: L
end subroutine auto_arima_predict
subroutine ar_predict(obj, inp, L, xpred, amse) bind(c, name='ar_predict')
import c_int, c_double, c_ptr
type(c_ptr), value :: obj
real(c_double) :: inp(*), xpred(*), amse(*)
integer(c_int), value :: L
end subroutine ar_predict
subroutine ar(inp, N, p, method, phi, var) bind(c, name='ar')
import c_int, c_double
real(c_double) :: inp, phi, var
integer(c_int), value :: N, p, method
end subroutine ar
! setMethod routines 🔻
subroutine arima_setMethod(obj, value) bind(c, name='arima_setMethod')
import c_int, c_ptr
type(c_ptr), value :: obj
integer(c_int), value :: value
end subroutine arima_setMethod
subroutine sarima_setMethod(obj, value) bind(c, name='sarima_setMethod')
import c_int, c_ptr
type(c_ptr), value :: obj
integer(c_int), value :: value
end subroutine sarima_setMethod
subroutine auto_arima_setMethod(obj, value) bind(c, name='auto_arima_setMethod')
import c_ptr, c_int
type(c_ptr), value :: obj
integer(c_int), value :: value
end subroutine auto_arima_setMethod
subroutine sarimax_setMethod(obj, value) bind(c, name='sarimax_setMethod')
import c_int, c_ptr
type(c_ptr), value :: obj
integer(c_int), value :: value
end subroutine sarimax_setMethod
! setOptMethod routines 🔻
subroutine arima_setOptMethod(obj, value) bind(c, name='arima_setOptMethod')
import c_int, c_ptr
type(c_ptr), value :: obj
integer(c_int), value :: value
end subroutine arima_setOptMethod
subroutine sarima_setOptMethod(obj, value) bind(c, name='sarima_setOptMethod')
import c_int, c_ptr
type(c_ptr), value :: obj
integer(c_int), value :: value
end subroutine sarima_setOptMethod
subroutine auto_arima_setOptMethod(obj, value) bind(c, name='auto_arima_setOptMethod')
import c_ptr, c_int
type(c_ptr), value :: obj
integer(c_int), value :: value
end subroutine auto_arima_setOptMethod
subroutine arima_vcov(obj, vcov) bind(c, name='arima_vcov')
import c_ptr, c_double
type(c_ptr), value :: obj
real(c_double) :: vcov
end subroutine arima_vcov
subroutine sarima_vcov(obj, vcov) bind(c, name='sarima_vcov')
import c_double, c_ptr
type(c_ptr) :: obj
real(c_double) :: vcov
end subroutine sarima_vcov
subroutine sarimax_vcov(obj, vcov) bind(c, name='sarimax_vcov')
import c_ptr, c_double
type(c_ptr), value :: obj
real(c_double) :: vcov
end subroutine sarimax_vcov
subroutine auto_arima_setApproximation(obj, approximation) bind(c, name='auto_arima_setApproximation')
import c_int, c_ptr
type(c_ptr), value :: obj
integer(c_int), value :: approximation
end subroutine auto_arima_setApproximation
subroutine auto_arima_setStepwise(obj, stepwise) bind(c, name='auto_arima_setStepwise')
import c_int, c_ptr
type(c_ptr), value :: obj
integer(c_int), value :: stepwise
end subroutine auto_arima_setStepwise
subroutine auto_arima_setStationary(obj, stationary) bind(c, name='auto_arima_setStationary')
import c_int, c_ptr
type(c_ptr) :: obj
integer(c_int), value :: stationary
end subroutine auto_arima_setStationary
subroutine auto_arima_setSeasonal(obj, seasonal) bind(c, name='auto_arima_setSeasonal')
import c_ptr, c_int
type(c_ptr), value :: obj
integer(c_int), value :: seasonal
end subroutine auto_arima_setSeasonal
subroutine auto_arima_setStationarityParameter(obj, test, alpha, type) bind(c, name='auto_arima_setStationarityParameter')
import c_ptr, c_double, c_char
type(c_ptr), value :: obj
character(c_char) :: test, type
!!\tocheck:
real(c_double), value :: alpha
end subroutine auto_arima_setStationarityParameter
subroutine auto_arima_setSeasonalParameter(obj, test, alpha) bind(c, name='auto_arima_setSeasonalParameter')
import c_ptr, c_double, c_char
type(c_ptr), value :: obj
character(c_char) :: test
real(c_double), value :: alpha
end subroutine auto_arima_setSeasonalParameter
subroutine auto_arima_setVerbose(obj, verbose) bind(c, name='auto_arima_setVerbose')
import c_int, c_ptr
type(c_ptr), value :: obj
integer(c_int), value :: verbose
end subroutine auto_arima_setVerbose
! summary routines 🔻
subroutine arima_summary(obj) bind(c, name='arima_summary')
import c_ptr
type(c_ptr), value :: obj
end subroutine arima_summary
subroutine sarima_summary(obj) bind(c, name='sarima_summary')
import c_ptr
type(c_ptr), value :: obj
end subroutine sarima_summary
subroutine sarimax_summary(obj) bind(c, name='sarimax_summary')
import c_ptr
type(c_ptr), value :: obj
!! `sarimax_set` struct
end subroutine sarimax_summary
subroutine auto_arima_summary(obj) bind(c, name='auto_arima_summary')
import c_ptr
type(c_ptr), value :: obj
end subroutine auto_arima_summary
subroutine ar_estimate(x, N, method) bind(c, name='ar_estimate')
import c_int, c_double
real(c_double) :: x
integer(c_int), value :: N, method
end subroutine ar_estimate
subroutine ar_summary(obj) bind(c, name='ar_summary')
import c_ptr
type(c_ptr), value :: obj
end subroutine ar_summary
subroutine model_estimate(x, N, d, pmax, h) bind(c, name='model_estimate')
import c_int, c_double
real(c_double) :: x
integer(c_int), value :: N, d, pmax, h
end subroutine model_estimate
subroutine arima_free(object) bind(c, name='arima_free')
!! free arima struct memory
import c_ptr
type(c_ptr), value :: object
!! pointer points to `arima_objectect`
end subroutine arima_free
subroutine sarima_free(object) bind(c, name='sarima_free')
!! free sarima struct memory
import c_ptr
type(c_ptr), value :: object
!! pointer points to `sarima_object`
end subroutine sarima_free
subroutine sarimax_free(object) bind(c, name='sarimax_free')
!! free sarimax struct memory
import c_ptr
type(c_ptr), value :: object
!! pointer points to `sarimax_object`
end subroutine sarimax_free
!> Free auto_arima struct memory
subroutine auto_arima_free(object) bind(c, name='auto_arima_free')
import c_ptr
!> Pointer points to `auto_arima_object`
type(c_ptr), value :: object
end subroutine auto_arima_free
subroutine ar_free(object) bind(c, name='ar_free')
!! free ar struct memory
import c_ptr
type(c_ptr), value :: object
!! pointer points to `ar_object`
end subroutine ar_free
! Yule-Walker, Burg and Hannan Rissanen Algorithms for Initial Parameter Estimation
subroutine yw(x, N, p, phi, var) bind(c, name='yw')
!! Yule-Walker Algorithms for Initial Parameter Estimation
import c_int, c_double
real(c_double) :: var
real(c_double) :: x(*), phi(*)
integer(c_int), value :: N, p
end subroutine yw
subroutine burg(x, N, p, phi, var) bind(c, name='burg')
!! Burg Algorithms for Initial Parameter Estimation
import c_int, c_double
real(c_double) :: var
real(c_double) :: x(*), phi(*)
integer(c_int), value :: N, p
end subroutine burg
subroutine hr(x, N, p, q, phi, theta, var) bind(c, name='hr')
!! Hannan Rissanen Algorithms for Initial Parameter Estimation
import c_int, c_double
real(c_double) :: x(*), phi(*), theta(*), var
integer(c_int), value :: N, p, q
end subroutine hr
end interface
contains
subroutine sarimax_exec(obj, inp, xreg)
type(c_ptr), intent(in) :: obj
real(c_double), intent(in) :: inp(*)
real(c_double), intent(out), optional, target :: xreg(*)
if (present(xreg)) then
call ctsa_sarimax_exec(obj, inp, c_loc(xreg))
else
call ctsa_sarimax_exec(obj, inp, c_null_ptr)
end if
end subroutine sarimax_exec
subroutine auto_arima_exec(obj, inp, xreg)
type(c_ptr), intent(in) :: obj
real(c_double), intent(in) :: inp(*)
real(c_double), intent(out), optional, target :: xreg(*)
if (present(xreg)) then
call ctsa_auto_arima_exec(obj, inp, c_loc(xreg))
else
call ctsa_auto_arima_exec(obj, inp, c_null_ptr)
end if
end subroutine auto_arima_exec
end module fortsa_model_m