I'm about to embark on training a neural network on daily forex data, with a view to obtaining a predictive network. I'm also interested in using data other than the forex currency pair data itself, in a manner similar to intermarket analysis. What other time series data does the panel think will provide meaningful input? Obviously, various other forex cross rates are important, along with perhaps interest rate time series. But what about perhaps less intuitively obvious time series? I'm more interested in time series that have a justifiably fundamental reason for inclusion rather than those that might simply exhibit historical correlation. Links to online references/papers, e.g. SSRN etc. would be very welcome.
In addition to FX currency cross rates and interest rates, several other potentially useful inputs are:
1) Economic data (GDP, inflation rates, employment figures) for the specific countries whose currencies you are interested in. While these data may be useful indicators, there are however two problems: Firstly the granularity of the data. If you are using EOD or intra-day FX data then ideally you would like the other inputs to your NN to be on a similar timescale and these are not. The second problem is that government statistics are often subject to "adjustment" some time after issue. Notwithstanding these caveats, such economic data may be useful with regard to long-term trends.
2) FX rates are relative values of one fiat currency against another without any absolute scale of "true" value. It is useful to also include as input "hard" asset price series such as precious and base metals and energy. These help to provide at least some form of absolute reference. For example a rising price of gold (denominated in USD) can alternatively be viewed as a decline in the value of the USD vs. hard assets.