Evaluation device for generating validation data for a recurrent neural network, wherein the recurrent neural network comprises connections of neurons (n) in a first layer of neurons (n) to neurons (n) in the same layer and/or in a second layer of neurons (n), comprising • an input interface in order to receive first data (x) from at least one environment capture sensor, wherein the first data (x) comprise time series data from an imaging environment capture sensor, wherein a time series at a given point in time (ti) respectively comprises a position (yi) of at least one object captured using the environment capture sensor, • a memory (12, 21) comprising a program comprising ∘ a first program section in order to generate second data (G(z)) on the basis of the first data (x), wherein the second data (G(z)) comprise generated time series data for the object, and ∘ a second program section which is programmed, while repeatedly executing the program, to receive the first data (x) and the second data (G(z)) as an input in order to learn a classification of the first data (x) as data from the at least one environment capture sensor and of the second data (G(z)) as generated data, • wherein the first program section is programmed, while repeatedly executing the program, to learn to generate the second data (G(z)) in such a manner that a second distribution function (p_G) of the second data (G(z)), which results from this data generation, in a characteristic of the second distribution function (p_G) is indistinguishable from a characteristic of the first distribution function (p(x)), • wherein the evaluation device is designed to execute the program, and • an output interface to the recurrent neural network in order to provide validation data for the recurrent neural network from the second distribution function (p G), wherein the validation data comprise data from the second distribution function (p_G) for training the recurrent neural network, and to train the recurrent neural network using said data. • an input interface in order to receive first data (x) from at least one environment capture sensor, wherein the first data (x) comprise time series data from an imaging environment capture sensor, wherein a time series at a given point in time (ti) respectively comprises a position (yi) of at least one object captured using the environment capture sensor, • a memory (12, 21) comprising a program comprising ∘ a first program section in order to generate second data (G(z)) on the basis of the first data (x), wherein the second data (G(z)) comprise generated time series data for the object, and ∘ a second program section which is programmed, while repeatedly executing the program, to receive the first data (x) and the second data (G(z)) as an input in order to learn a classification of the first data (x) as data from the at least one environment capture sensor and of the second data (G(z)) as generated data, ∘ a first program section in order to generate second data (G(z)) on the basis of the first data (x), wherein the second data (G(z)) comprise generated time series data for the object, and ∘ a second program section which is programmed, while repeatedly executing the program, to receive the first data (x) and the second data (G(z)) as an input in order to learn a classification of the first data (x) as data from the at least one environment capture sensor and of the second data (G(z)) as generated data, • wherein the first program section is programmed, while repeatedly executing the program, to learn to generate the second data (G(z)) in such a manner that a second distribution function (p_G) of the second data (G(z)), which results from this data generation, in a characteristic of the second distribution function (p_G) is indistinguishable from a characteristic of the first distribution function (p(x)), • wherein the evaluation device is designed to execute the program, and • an output interface to the recurrent neural network in order to provide validation data for the recurrent neural network from the second distribution function (p G), wherein the validation data comprise data from the second distribution function (p_G) for training the recurrent neural network, and to train the recurrent neural network using said data.