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Dynamic Boltzmann Machine (DyBM) has been shown highly efficient to predicttime-series data. Gaussian DyBM is a DyBM that assumes the predicted data isgenerated by a Gaussian distribution whose first-order moment (mean)dynamically changes over time but its second-order moment (variance) is fixed.However, in many financial applications, the assumption is quite limiting intwo aspects. First, even when the data follows a Gaussian distribution, itsvariance may change over time. Such variance is also related to importanttemporal economic indicators such as the market volatility. Second, financialtime-series data often requires learning datasets generated by the generalizedGaussian distribution with an additional shape parameter that is important toapproximate heavy-tailed distributions. Addressing those aspects, we show howto extend DyBM that results in significant performance improvement inpredicting financial time-series data.

Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions