The idPredict class represents an interesting and potentially innovative approach to time series processing, which may offer specific advantages over Transformers and CNNs depending on the problem at hand.

idpredict.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class IdPredictPyTorch(nn.Module):
    def __init__(self, input_shape, predict_steps=30, groups=512, layers=20, groups_size=20, no_skip=False):
        super(IdPredictPyTorch, self).__init__()
        self.predict_steps = predict_steps
        self.groups = groups
        self.layers = layers
        self.groups_size = groups_size
        self.no_skip = no_skip
        self.n = 0

        self.groups_d = nn.ParameterList([nn.Parameter(torch.randn(groups * predict_steps, input_shape[1], 1) * 0.01) for _ in range(layers)])
        self.groups_l = nn.ParameterList([nn.Parameter(torch.randn(groups * predict_steps, 1, input_shape[2]) * 0.01) for _ in range(layers)])
        self.groups_ff = nn.ParameterList([nn.Parameter(torch.randn(input_shape[1], input_shape[2]) * 0.01) for _ in range(layers)])
        self.groups_b = nn.ParameterList([nn.Parameter(torch.rand(1) * 0.2 + 0.2) for _ in range(layers)])

    def forward(self, input):
        self.n += 1
        result = None
        for k in range(self.layers):
            vd = torch.tanh(self.groups_d[k])
            vl = torch.tanh(self.groups_l[k])
            b = self.groups_b[k]
            r = torch.matmul(vd, vl)



            # Adjust the slicing and concatenation logic as per PyTorch's conventions
            # This is a simplified conversion; you'll need to adjust it based on your actual input shapes and operations
            r2 = input[:, :, self.predict_steps:] + r

            if result is not None:
                result += r2 * self.groups_ff[k] * b
            else:
                result = r2 * self.groups_ff[k] * b

        return result

# Example usage
# You need to adjust `input_shape` based on your actual input data
input_shape = (batch_size, feature_dim, sequence_length)
model = IdPredictPyTorch(input_shape=input_shape)