Exogenous variables

• The ARMA processes are flexible models for a time series \(Y_t\), \(t=1,\ldots,n\) evolving randomly over time, but they do not include the possibility that anything is influencing \(Y_t\).
• An exogeneous variable is another variable, say \(X_t\), that influences the behaviour of \(Y_t\)
  • Wind power production \(Y_t\) is influenced by the wind speed \(X_t\)
  • The velocity of a DC motor \(Y_t\) is influenced by the input voltage \(X_t\)
• Here \(X_t\) may be another stochastic process, which we do not model, but only consider as given, or it might be something we can control.

Data example

• The dataset below contains data from Jan 7 to Jul 13 2022 on two variables
  • forecast: Total day ahead forecasted wind and solar energy production
  • price: Day ahead elspot prices with weekly variation removed

elspot<-read.csv("https://asta.math.aau.dk/eng/static/datasets?file=elspot.csv", header = TRUE)
forecast<-elspot[,2]
price<-elspot[,3]
ts.plot(ts(forecast),ts(-price),col=1:2)
legend("topright",legend=c("forecast","- price"),col=1:2,lty=1)

plot(forecast,price)

Regression models with exogenous variables

• We can combine regression models with ARMA models to obtain a stochastic process which is influenced by exogenous variables.
• Consider a linear regression of \(Y_t\) on \(X_t\), but where the noise term is an ARMA process: \[ Y_t = \gamma_0 + \gamma_1 X_t + \epsilon_t, \qquad \alpha(B)\epsilon_t = \beta(B)W_t \]
• If we isolate \(\epsilon_t = Y_t - \gamma_0 - \gamma_1 X_t\) and insert into the ARMA expression, we get something that looks more like an ARMA process, but with \(Y_t\) adjusted by the exogenous variable: \[ \alpha(B)(Y_t-\gamma_0 - \gamma_1 X_t) = \beta(B)W_t \]
• The purpose of fitting such a model is both to obtain a good model for the evolution of the data and to obtain an understanding of the relation between \(Y_t\) and \(X_t\).
• Above, \(X_t\) is a single stochastic process, but we can also include multiple stochastic processes by making a multiple regression model with an ARMA model for the errors.

Example

• As an example consider a simple linear regression combined with an AR(1) process for noise terms: \[ Y_t = \gamma_0 + \gamma_1 X_t + \epsilon_t, \qquad \epsilon_t = \alpha_1 \epsilon_{t-1}+W_t \] or, since \(\epsilon_{t-1} = Y_{t-1} - \gamma_0 -\gamma_1 X_{t-1}\), \[ Y_t = \alpha_1Y_{t-1} + (1-\alpha_1)\gamma_0 + \gamma_1(X_t-\alpha_1 X_{t-1}) + W_t \]
• Notice that the model behaves like an AR(1) process, but instead of having a constant mean of 0, its mean is constantly adjusted by the exogenous variable.

Simulation of the example

• We simulate some data resembling the example, where we let \(X_t\) follow a sine curve:

alpha = 0.9; gamma = 1; n = 100
x = as.ts(5*sin(1:n/5))
eps = arima.sim(model=list(ar=alpha),n=n)
y = gamma*x+eps
ts.plot(x,y,col=1:2)

• We should think of the red curve as some data we want to model, and the black curve as another variable which we believe may influence the data.
• We can also plot \(X_t\) against \(Y_t\) to get a view of the relation between the two variables.

plot(as.numeric(x),as.numeric(y))

Estimation and model checking

• We can estimate the parameters using the arima function in R.
• We fit a linear regression model with AR(1) noise to the simulated data (i.e. the true model used for simulation):

mod=arima(y,order=c(1,0,0),xreg=x); mod

## 
## Call:
## arima(x = y, order = c(1, 0, 0), xreg = x)
## 
## Coefficients:
##          ar1  intercept       x
##       0.8069     0.0679  1.0551
## s.e.  0.0569     0.4795  0.1018
## 
## sigma^2 estimated as 0.923:  log likelihood = -138.41,  aic = 284.82

• The fitted model becomes \[ Y_t = 0.0679 + 1.0551 \cdot X_t + \epsilon_t, \qquad \epsilon_t = 0.8069 \cdot \epsilon_{t-1}+W_t \]
• The errors \(\hat{\epsilon}_t = y_t -0.0679 + 1.0551 \cdot x_t\) should behave like an AR(1)-model with \(\hat{\alpha} = 0.8069\).
  • So the residuals \(\epsilon_t - 0.8069 \cdot \epsilon_{t-1}\) should look like white noise.

plot(resid(mod))

acf(resid(mod))

Fitting AR(1) model to data example

• Recall the elspot price dataset

forecast<- ts(forecast)
price<-ts(price)
model=arima(price,order=c(1,0,0),xreg=forecast); model

## 
## Call:
## arima(x = price, order = c(1, 0, 0), xreg = forecast)
## 
## Coefficients:
##          ar1  intercept  forecast
##       0.3886  1715.8412   -0.3053
## s.e.  0.0680    73.2894    0.0271
## 
## sigma^2 estimated as 117486:  log likelihood = -1364.2,  aic = 2736.41

• So we get the model \[\text{price}_t = 1715.8412 - 0.3053\cdot \text{forecast}_t +\epsilon_t, \qquad \epsilon_t = 0.3886\cdot \epsilon_{t-1} + W_t.\]

plot(resid(model))

acf(resid(model))

• Residuals indicate that there could be some weekly variation not accounted for.

Prediction

• Prediction can only be performed if we know the behavior of \(X_t\) for future time points, for example if we are able to control it.
• For the previous example we assume that the sine curve continues:

nnew = 20
xnew = lag(as.ts(5*sin(((n+1):(n+nnew))/5)),-n)
ts.plot(x,y,xnew,col=c(1,2,1),lty=c(1,1,2))

• We use the predict function.

p = predict(mod,n.ahead=nnew,newxreg=xnew)
ts.plot(x,y,xnew,p$pred,p$pred+2*p$se,p$pred-2*p$se,col=c(1,2,1,2,2,2),lty=c(1,1,2,2,3,3))

An example with delay

• If we model the influence of \(X_t\) on \(Y_t\), it may take some time before \(Y_t\) responds to a change in \(X_t\).
  • Say the delay is \(k\) time steps.
    
• We want to model the effect of \(X_{t-k}\) on \(Y_t\).
  • We may not know the delay \(k\), so we may need to estimate it first.
• We simulate a dataset with a built-in delay, and then we model this afterwards.

alpha = 0.5; gamma = 1; n = 100; delay = 5
x = as.ts(5*sin(1:(n+delay)/5))
eps = arima.sim(model=list(ar=alpha),n=n+delay)
y = gamma*lag(x,-delay)+eps
dat_lag = ts.intersect(x,y)
ts.plot(dat_lag[,1],dat_lag[,2],col=1:2)

The cross-correlation function

• The cross correlation function is used for checking the relation between two time series at different time points: \[ \rho_{xy}(t+k,t) = \text{Cor}(X_{t+k},Y_{t}). \]

• Values that are close to 1 or -1 indicate that the two time series are closely related if \(X_t\) is delayed by \(k\) time steps.

• Cross-correlation function for the simulated data

cc = ccf(dat_lag[,1],dat_lag[,2],lag.max=10)

• Cross-correlation function for the elspot data:

ccf(forecast,price)

Fitting models with lag

• We estimate the lag to be the one where the cross-correlation function is maximal:

estlag = cc$lag[which(cc$acf==max(abs(cc$acf)))]
estlag

## [1] -5

• Plotting the data with this lags can be useful to check the choice:

dat_shifted = ts.intersect(lag(as.ts(dat_lag[,1]),estlag),dat_lag[,2] )
ts.plot(dat_shifted[,1],dat_shifted[,2],col=1:2)

• We can now fit a model with this lag:

mod=arima(dat_shifted[,2],order=c(1,0,0),xreg=dat_shifted[,1]); mod

## 
## Call:
## arima(x = dat_shifted[, 2], order = c(1, 0, 0), xreg = dat_shifted[, 1])
## 
## Coefficients:
##          ar1  intercept  dat_shifted[, 1]
##       0.5938    -0.2047            1.0526
## s.e.  0.0820     0.2347            0.0615
## 
## sigma^2 estimated as 0.8884:  log likelihood = -129.39,  aic = 266.79

ARMAX models

• An alternative way of including exogenous variables into an ARMA model is an ARMAX model.
• The \(ARMAX(p,q,b)\) model is an \(ARMA(p,q)\) model including \(b\) terms of an exogenous variable, i.e. it is defined by \[ Y_t = \sum_{i=1}^p \alpha_i Y_{t-i} + \sum_{i=1}^b \gamma_i X_{t-i} + W_t + \sum_{i=1}^q \beta_i W_{t-i} \]
• Using the backshift operator, this can be written as \[ \alpha(B)Y_t = \gamma(B) X_t + \beta(B) W_t \] with \(\alpha(B)=1-\sum_{i=1}^p \alpha_i B^i\), \(\beta(B)=1+\sum_{i=1}^q \beta_i B^i\), and \(\gamma(B)=\sum_{i=1}^b \gamma_i B^i\).
• Compare with the regression with ARMA noise: \[ \alpha(B)(Y_t-\gamma_0-\gamma_1 X_t) = \beta(B)W_t \quad \Rightarrow \quad \alpha(B)Y_t = \alpha(B)(\gamma_0 + \gamma_1\gamma X_t) + \beta(B)W_t \]
• The difference is only how the model includes the exogenous variable.
• It is mostly a matter of taste which kind of model you should choose.
• Only the regression with ARMA noise is included into R as standard.

Discrete vs. continuous time

There are two fundamentally different model classes for time series data.

• Discrete time stochastic processes
  • Variables given at equally spaced time points
• Continuous time stochastic processes
  • Variables that evolve over a continuous time scale

So far we have only looked at the discrete time case. We will finish todays lecture by looking a bit at the continuous time case, just to give you an idea of this topic.

Continuous time stochastic processes

• In this setup we see the underlying \(X_t\) as a continuous function of \(t\) for \(t\) in some interval \([0,T]\).

• In principle we imagine that there are infinitely many data points, simply because there are infinitely many time points between 0 and \(T\).

• In practice we will always only have finitely many data points.

• But we can imagine that the real data actually contains all the data points. We are just not able to measure them (and to store them in a computer).

• With a model for all datapoints, we are - through simulation - able to describe the behaviour of data. Also between the observations.

The Wiener process

• A key example of a process in continuous time will be the so–called Wiener process or Brownian motion.

• Here are three simulated realizations (black, blue and red) of this process: here

## Package 'Sim.DiffProc', version 4.8
## browseVignettes('Sim.DiffProc') for more informations.

• A Wiener process has the following properties:
  • It starts in 0: \(B_0=0\).
  • It has independent increments: For \(0<s<t\) it holds that \(B_t-B_s\) is independent of everything that has happened up to time \(s\), that is \(B_u\) for all \(u\leq s\).
  • It has normally distributed increments: For \(0<s<t\) it holds that the increment \(B_t-B_s\) is normally distributed with mean zero and variance \(t-s\): \[B_t-B_s \sim \texttt{norm}(0,t-s).\]
• The intuition of this process is that it somehow changes direction all the time: How the process changes after time \(s\) will be independent of what has happened before time \(s\). So whether the process should increase or decrease after \(s\) will not be affected by how much it was increasing or decreasing before. This gives the very bumpy behaviour over time.

Stochastic differential equations

• A common way to define a continuous time stochastic process model is through a stochastic differential equation (SDE) which we will turn to shortly, but before doing so we will recall some basic things about ordinary differential equations.

• Example: Suppose \(f\) is an unknown differentiable function satisfying the differential equation \[\frac{df(t)}{dt}=-4f(t)\] with initial condition \(f(0)=1\). This equation has the solution \[f(t)=\exp(-4t)\]

• With a slightly unusual notation we can rewrite this as \[df(t)=-4\cdot f(t)dt\]

• This equation has the following (hopefully intuitive) interpretation:

  • When time increases by a small amount \(dt\) (from \(t\) to \(t + dt\)) the value of \(f\) changes (approximately) by \(-4f(t)\cdot dt\).

• So when \(t\) is increased, then \(f\) is decreased, and the decrease is proportional to the value of \(f(t)\). That is why \(f\) decreases slower and slower, when \(t\) is increased.

• We say that the function has a drift towards zero, and this drift is determined by the value of the function.

Stochastic differential equations

• It will probably never be true that data behaves exactly like the exponentially decreasing curve on the previous slide.

• Instead we will consider a model, where some random noise from a Wiener process has been added to the growth rate. Two different (black/blue) simulated realizations can be seen below

• The type of process that is simulated above is described formally by the equation \[d X_t=-4 X_tdt+0.1d B_t\]

• This is called a Stochastic Differential Equation (SDE), and the processes simulated above are called solutions of the stochastic differential equation.

• The SDE \(d X_t=-4 X_tdt+0.1d B_t\) has two terms:

  • \(-4X_t dt\) is the drift term.
  • \(0.1dB_t\) is the diffusion term.

• The intuition behind this notation is very similar to the intuition in the equation \(df(t)=-4\cdot f(t)\;dt\) for an ordinary differential equation. When the time is increased by the small amount \(dt\), then the process \(X_t\) is increased by \(-4X_t\,dt\) AND by how much the process \(0.1B_t\) has increased on the time interval \([t,t+dt]\).

• So this process has a drift towards zero, but it is also pushed in a random direction (either up or down) by the Wiener process (more precisely, the process \(0.1B_t\))