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Numerous studies on time series prediction have been undertaken by a variety of researchers. Most of them typically used unidirectional computation flow, that is, present signals are applied to the model as an input and predicted future signals are derived from the model as an output. On the contrary, bidirectional computation style has been proposed recently and applied to prediction tasks. A bidirectional neural network model consists of two mutually connected subnetworks and performs direct and inverse transformations bidirectionally. To apply this model to time series prediction tasks, one subnetwork is trained a conventional future prediction task and the other is trained an additional task for past prediction. Since the coupling effects between the future and past prediction subsystems promote the model's signal processing ability, bidirectionalization of the computing architecture makes it possible to improve its performance. Furthermore, in order to investigate the acquired signal transformation, two kinds of chaotic time series—the Mackey–Glass time series and “Data Set A”—are adopted in this paper. As a result of computer simulations, it has been found experimentally that the direct and inverse transformations developed independently and their information integration give the bidirectional model an advantage over the unidirectional one. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 145(3): 50–60, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10232