  Sequential data is being generated at an unprecedented pace in various forms,
including text and genomic data. This creates the need for efficient
compression mechanisms to enable better storage, transmission and processing of
such data. To solve this problem, many of the existing compressors attempt to
learn models for the data and perform prediction-based compression. Since
neural networks are known as universal function approximators with the
capability to learn arbitrarily complex mappings, and in practice show
excellent performance in prediction tasks, we explore and devise methods to
compress sequential data using neural network predictors. We combine recurrent
neural network predictors with an arithmetic coder and losslessly compress a
variety of synthetic, text and genomic datasets. The proposed compressor
outperforms Gzip on the real datasets and achieves near-optimal compression for
the synthetic datasets. The results also help understand why and where neural
networks are good alternatives for traditional finite context models
