I'm training a recurrent network on a stock price time series. As you can imagine, the price increases with time. I think the importance of the bias decreases as the stock increases, especially since the test set has an ever higher price than the end of the training set.

Is it a good idea to "discourage" the learning via bias by using heavy regularization, or disabling the bias entirely? Here's my architecture:

class ForecastModel(Model):
    def __init__(self):
        super(ForecastModel, self).__init__()
        self.layer0 = Dense(128, activation='relu', dtype=tf.float32)
        self.layer1 = LSTM(256, return_sequences=True, dtype=tf.float32,
        self.layer2 = GRU(512, return_sequences=True, dtype=tf.float32,
        self.layer3 = SimpleRNN(1024, dtype=tf.float32,
        self.layer4 = Dense(2096, activation='relu', dtype=tf.float32)
        self.flat = Flatten()
        self.concat = Concatenate()
        self.layer5 = Dense(1, dtype=tf.float32)

    def __call__(self, inputs, training=None, **kwargs):
        a = self.layer0(inputs)
        b = self.layer1(inputs)
        b = self.layer2(b)
        b = self.layer3(b)
        a = self.flat(a)
        a = self.layer4(a)
        x = self.concat([a, b])
        x = self.layer5(x)
        return x

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