# Forecasting product of two time series with correlation

I am trying to forecast the product two time series. That is, given $\{x_t\}_{t=0}^{T-1}, \{y_t\}_{t=0}^{T-1}$, forecast $x_T\cdot y_T$. The two time series have minimal but nontrivial correlation ~0.1. I can apply standard time series forecasting techniques individually and then multiply two independent forecasts but that seems to introduce a lot of bias due to the correlation.

What is standard practice in this case? The data can be assumed to be stationary.

• How do you forecast the individual series? Dec 15 '14 at 0:56
• When you say the series 'have correlation', are you talking about their autocorrelation or about $\text{corr}(x_t,y_t)$, or about something else? Dec 15 '14 at 0:58
• @Aksakal I am currently forecasting individual series with arima. Dec 15 '14 at 3:16
• @Glen_b I am talking about $corr(x_t, y_t)$ Dec 15 '14 at 3:17
• Does your arima have exogenous variables? Dec 15 '14 at 3:18

Estimate individual ARIMA models, obtain their residuals (innovations) $e_{xt}$ and $e_{yt}$.
From here you have a couple of options. The simplest one is to get the correlation of the residuals $\rho$, then use for forecasting. For instance, you can use the standard Cholesky decomposition method to produce correlated paths $e_{xt}$, $e_{yt}$ for Monte Carlo simulation, then plug them into ARIMA to forecast future values.
Of course, if $\rho$ is very small, you may consider ignoring it. Look at the statistical significance of your correlation coefficient. The chances are that it'll be not significant. Note, that correlation of series $x_t,y_t$ doesn't have to be the same as of $e_x,e_y$. This is especially true if you have exogenous variables, i.e. use ARIMAX model, because in this case the exogenous variables may capture the large part of correlation. Note also, that even if the series do not share exogenous variables, the variables could be correlated. For instance, GDP is correlated with Unemployment.