Missing Data with Recurrent Networks Handling Asynchronous or Missing Data with Recurrent Networks
An important issue with many sequential data analysis problems such as those encoun tered in nancial data sets is that di erent variables are known at di erent frequencies at di erent times asynchronicity or are sometimes missing To address this issue we propose to use recurrent networks with feedback into the input units based on two fun damental ideas The rst motivation is that the lled in value of the missing variable may not only depend in complicated ways on the value of this variable in the past of the sequence but also on the current and past values of other variables The second motivation is that for the purpose of making predictions or taking decisions it is not always necessary to ll in the best possible value of the missing variables In fact it is su cient to ll in a value which helps the system make better predictions or decisions The advantages of this approach are demonstrated through experiments on several tasks Missing Data with Recurrent Networks