Hi! I am new to Pyro and I have some questions modeling a hierarchical time-series model.
My dataset consists of several time series data sampled at different locations. However, data for some of the locations are completely missing. I would like to impute the missing data with a hierarchical Bayesian model. In particular, I would like to start with a linear model with one covariate.
I am basically following the examples in the Pyro documentations on forecasting. However, the examples only consider data at one location, while I need to model multiple locations together. In addition, the examples focus on predicting for future times, while I need to “predict” for new locations. Hence, the examples do not apply directly.
Following is my attempt:
def model(self, zero_data, covariates): data_size, duration, feature_dim = covariates.shape bias_scale = pyro.sample("bias_scale", dist.LogNormal(-5,5)) weight_scale = pyro.sample("weight_scale", dist.LogNormal(-5,5)) with pyro.plate("load", data_size, dim=-2): bias = pyro.sample("bias", dist.Normal(0, bias_scale)) weight = pyro.sample("weight", dist.Normal(0, weight_scale).expand([feature_dim]).to_event(1)) prediction = bias + (weight * covariates).sum(-1, keepdim=True) noise_scale = pyro.sample("noise_scale", dist.LogNormal(-5, 5).expand().to_event(1)) noise_dist = dist.Normal(0, noise_scale) self.predict(noise_dist, prediction)
The issue with this attempt is the “prediction”. Since my “covariates” has the shape (locations, duration, features), it does not work correctly with the “*” operator. In addition, I don’t know if my way of modeling the noise would result in independent noise wrt different locations and different time or not.
I am sorry if my expression is not that clear, and please tell me if I need to provide more details of this problem. I appreciate any suggestions and advice.
Thank you very much!