In demonejackson/CoFES_SNARX: Seasonal NARX via Keras

knitr::opts_chunk$set(
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Data Preparation: narxdataprep

Overview

CoFESNARXdata and CoFESSNARXdata prepare your data for the CoFES NARX and CoFES SNARX networks respectively. CoFESNARXdata returns the the number of target lags ($d_y$) and exegenous lags ($d_x$) that you request in order to run the NARX model that you desire while the CoFESSNARXdata returns the same but also adds a seasonal lag (s) for the target series. Both functions allows users to set the number of lags in the future that the target series via the (delay=1) parameter.

Utility Functions

There are two utility functions: (returnTARGET(data),returnX(data,T))

returnTARGET(data) takes the data frame created by CoFESNARXdata or CoFESSNARXdata and returns the target series as a dataframe.
returnX(data,T)takes the data frame created byCoFESNARXdata or CoFESSNARXdata and returns the regressors in a dataframe for your respective network.

Narx: $$y[t+delay]=f(x[t-d_x],...,x[t-1],x[t], y[t-d_y],...,y[t-l], \Theta)$$

($d_y$) The number of passed values/lags of $y_t$ used to predict $y_{(t+delay)}$
($d_x$) The number of passed values/lags of $x_t$ used to predict $y_{(t+delay)}$
(delay=1) The number of lags in the future that the networks predicts. ex) $y_{(t+3)}$

SNarx:
$$y[t+delay]=f(x[t-d_x],...,x[t-1],x[t],\underline{y[t-s]},y[t-d_y],...,y[t-l], \Theta)$$

(s): Seasonal Lag of $y_t$ used to predict $y_{(t+delay)}$

CoFESNARXdata Exmple (NARX data prep)

1) With Date

library(CoFESSNARX)
library(dplyr)

set.seed(123)
N <- 450 
n <- seq(1:N)
a <- n/10+4*sin(n/10)+sample(-1:6,N,replace=T)+rnorm(N)
b <- n/8+4*cos(n/10)+sample(-3:3,N,replace=T)+rnorm(N) 
c <- n/6+4*sin(n/10)+sample(-5:1,N,replace=T)+rnorm(N)
y <- (a+b+c)/3+rnorm(N)


date<-seq(as.Date("2018-1-1"), as.Date("2019-3-26"), by="days")

x <- data.frame(date,a,b,c)  
y <- data.frame(date, y)

dx <- 2 # The AR order for the Exegenous series 
dy <- 2 # The AR order for the Target series 
delay <- 10 # The number of time steps that the target is in the future
date <- T # Date or no Date Boolean

data<-CoFESSNARX::CoFESNARXdata(y,x,dy,dx,delay,date)


y <- CoFESSNARX::returnTARGET(data)
x <- CoFESSNARX::returnX(data,T)
print(head(y))
print(head(x))

1) Without date

library(CoFESSNARX)
library(dplyr)
set.seed(123)
N <- 450 
n <- seq(1:N)
a <- n/10+4*sin(n/10)+sample(-1:6,N,replace=T)+rnorm(N)
b <- n/8+4*cos(n/10)+sample(-3:3,N,replace=T)+rnorm(N) 
c <- n/6+4*sin(n/10)+sample(-5:1,N,replace=T)+rnorm(N)
y <- (a+b+c)/3+rnorm(N)


x <- data.frame(a,b,c)
y <- data.frame(y)


dx <- 2 # The AR order for the Exegenous series 
dy <- 2 # The AR order for the Target series 
delay <- 10 # The number of time steps that the target is in the future
date <- F # Date or no Date Boolean

data<-CoFESSNARX::CoFESNARXdata(y,x,dy,dx,delay,date)

head(data)


y <- CoFESSNARX::returnTARGET(data)
x <- CoFESSNARX::returnX(data,F)
print(head(y))
print(head(x))

CoFESSNARXdata Exmple (SNARX data prep)

1) With Date

library(CoFESSNARX)
library(dplyr)

set.seed(123)
N <- 450 
n <- seq(1:N)
a <- n/10+4*sin(n/10)+sample(-1:6,N,replace=T)+rnorm(N)
b <- n/8+4*cos(n/10)+sample(-3:3,N,replace=T)+rnorm(N) 
c <- n/6+4*sin(n/10)+sample(-5:1,N,replace=T)+rnorm(N)
y <- (a+b+c)/3+rnorm(N)


date<-seq(as.Date("2018-1-1"), as.Date("2019-3-26"), by="days")

x <- data.frame(date,a,b,c)
y <- data.frame(date, y)


dx <- 2 # The AR order for the Exegenous series 
dy <- 2 # The AR order for the Target series 
delay <- 10 # The number of time steps that the target is in the future
date <- T # Date or no Date Boolean
s <- 12

data<-CoFESSNARX::CoFESSNARXdata(y,x,dy,dx,delay,s,date)

head(data)


y <- CoFESSNARX::returnTARGET(data)
x <- CoFESSNARX::returnX(data,T)
print(head(y))
print(head(x))

1) Without date

library(CoFESSNARX)
library(dplyr)

set.seed(123)
N <- 450 
n <- seq(1:N)
a <- n/10+4*sin(n/10)+sample(-1:6,N,replace=T)+rnorm(N)
b <- n/8+4*cos(n/10)+sample(-3:3,N,replace=T)+rnorm(N) 
c <- n/6+4*sin(n/10)+sample(-5:1,N,replace=T)+rnorm(N)
y <- (a+b+c)/3+rnorm(N)


x <- data.frame(a,b,c)
y <- data.frame(y)


dx <- 2 # The AR order for the Exegenous series 
dy <- 2 # The AR order for the Target series 
delay <- 10 # The number of time steps that the target is in the future
date <- F # Date or no Date Boolean
s <- 12
data<-CoFESSNARX::CoFESSNARXdata(y,x,dy,dx,delay,s,date)

head(data)


y <- CoFESSNARX::returnTARGET(data)
x <- CoFESSNARX::returnX(data,F)
#x <- CoFESSNARX::returnX(data,F)
print(head(y))
print(head(x))