The problem of preserving autocorrelation and volatility characteristics in reputational time series forecasting is approached by Block Bootstrapping. Sentiment time series are tokenised by partitioning them into small blocks, and classifying each block as one of five characteristic shapes. LSTM is used to generate sequences of single tokens, each associated with a small data block. Forecasts are built by concatenating those small data blocks. The generative model is evaluated relative to dedicated autocorrelation and volatility criteria, and is compared with non-generative models in which blocks are selected from the historic data at random. Evaluations based on 130 reputation time series show that the generative model is optimally performant for volatility, up to a range of approximately six months. Success rates are in the range 91–99%. Autoregression characteristics are preserved up to a range of 2.5 months (88–100% success) and diminish to 65% at a 6-month horizon. The result is important for mitigating adverse reputational risk and corporate strategy.