I want to build a CNN for classifying time series data with high accuracy. The data has been windowed into chunks of 12 time steps, it looks like this:
       [0.        , 0.00167084, 0.00568087, ..., 0.60016708, 0.55238095,
        0.68421053],
       [0.00167084, 0.00568087, 0.146533  , ..., 0.55238095, 0.68421053,
        0.6566416 ],

Consider my use of CNN for such data an experiment.
Here are 2 architectures that I have tried. But they have poor accuracy.
#Architecture 1:
init:

    self.conv1 = nn.Conv1d(1,3, kernel_size = 2, stride = 1, padding =1)
    self.bn1 = nn.BatchNorm1d(3)
    self.maxpool1 = nn.MaxPool1d(kernel_size=2, stride=2, padding=1)
    self.dropout1 = nn.Dropout(0.3)
    
    self.conv2 = nn.Conv1d(3, 5, kernel_size=2, stride=2, padding=1)
    self.bn2 = nn.BatchNorm1d(5)
    self.dropout2 = nn.Dropout(0.3)
    
    self.conv3 = nn.Conv1d(5, 5, kernel_size=2, stride=1, padding=1)
    self.bn3 = nn.BatchNorm1d(5)
    self.dropout3 = nn.Dropout(0.3)
    
    self.fc = nn.Linear(5, num_classes)


forward:

    x = self.conv1(x)
    x = self.bn1(x)
    x = F.relu(x)

    x = self.maxpool1(x)
    x = self.dropout1(x)

    x = self.conv2(x)
    x = self.bn2(x)
    x = F.relu(x)

    x = self.dropout2(x)

    x = self.conv3(x)
    x = self.bn3(x)
    x = F.relu(x)

    x = self.dropout3(x)
    x = torch.mean(x, dim = 2)
    x = self.fc(x)


#Architecture 2: similar to PyTorch MNIST example
init:

    self.conv1 = nn.Conv1d(1,3, kernel_size = 2, stride = 1, padding =1)
    self.conv2 = nn.Conv1d(3, 5, kernel_size=2, stride=2, padding=1)
    self.maxpool1 = nn.MaxPool1d(kernel_size=2, stride=2, padding=1)
    self.dropout1 = nn.Dropout(0.3)
    self.dropout2 = nn.Dropout(0.3)
    
    self.fc1= nn.Linear(20, 12)
    self.fc2= nn.Linear(12, num_classes)


forward:

    x = self.conv1(x)
    x = F.relu(x)
    x = self.conv2(x)
    x = F.relu(x)
    x = self.maxpool1(x)
    x = self.dropout1(x)
    x = torch.flatten(x, 1)
    x = self.fc1(x)
    x = F.relu(x)
    x = self.dropout2(x)
    x = self.fc2(x) 


Could anyone suggest what I am doing fundamentally wrong here?
P.S. I want to classify them into 3 classes