Inverted Dirichlet State Space Model for Time Series Forecasting
Omar Graja, Fatma Najar, Manar Amayri, Nizar Bouguila · 2021
In this work, we build a new time series state space model based on inverted Dirichlet distribution. We use the power steady modeling approach and we derive an analytical expression of the model latent variable using the maximum a posteriori technique. We also approximate the predictive density using local variational inference, and we validate our model on the electricity consumption time series dataset of Germany. A comparison with the generalized Dirichlet state space model is conducted, and the results demonstrate the merits of our approach in modeling continuous positive vectors.