Mobile operators collect and store the network generated traffic data for analysis. Time Series Prediction (TSP) has been used in mobile network traffic data analysis to produce predictive results for network planning and resource allocation. We propose a novel method of predicting mobile network traffic using neural networks based on conditional probability modeling between adjacent data windows. Firstly, we develop a pre-processing method to aggregate the raw traffic log data and sample the aggregated time series to adjacent data windows, as training samples. Secondly, we use neural networks to parameterize the conditional probability between adjacent data windows and estimate the probability by training the neural networks with sampled data. The estimated conditional probability is then used to ensemble the prediction. Thirdly, we show theoretically that the prediction based on all historical data is equivalent to the prediction based on just previous data window, given the estimation of conditional probability between adjacent data windows. We also analyze computation complexity and show that seasonality will reduce the computational complexity. In the experiment, we compare the prediction performance among the models with different seasonality, sample size and number of hidden layers, and show that the proposed schemes achieve better prediction accuracy than state-of-the-art. IEEE