Page Comparison

Metadata incorporation and results:

We have modified DA-RNN to utilize meta-data by including a merging layer in the encoder_layer. This module can be called with several different properties:

class MetaMerger(nn.Module):
    def __init__(self, meta_params, meta_method, embed_shape, in_shape):
        super().__init__()
        self.method_layer = meta_method
        if meta_method == "down_sample":
            self.initial_layer = torch.nn.Linear(embed_shape, in_shape)
        elif meta_method == "up_sample":
            self.initial_layer = torch.nn.Linear(in_shape, embed_shape)
        self.model_merger = MergingModel(meta_params["method"], meta_params["params"])

Lets review the methods you can use:

“down_sample”: In this method we use a linear layer to map the embedding representation to an Tensor of size (batch_size, seq_len, n_feature_time_series-1). The Bilinear layer then operates along the seq_len dimension as it will then be resized to (batch_size, 1, n_feature_series-1).

“up_sample”: The reverse of down sample scales the features back to embedding_dim.

“repeat”: Repeats the meta-data across the temporal dimension. So for instance if the temporal data is dimension (batch_size, seq_len, n_feature_time_series) then we will repeat the meta-data to be (batch_size, seq_len, embedding_dim).

COVID-19 Forecasting Results (no pre-training):

COVID-19 Forecasting Results (with pre-training first on other 240 other counties):

River flow forecasting (no pre-training)