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I have trained the time series prediction model using old data, and as time goes by, I get more data and want to update my model with it. But it seems like my model perform worse as I update the model.

Here is what I did. First I trained with the existing data.

model = build()
model.compile(optimizer='adam', loss='mse')
model.fit(X_old, y_old, validation_data = (X_old_val, y_old_val), epochs=100, batch_size=32, verbose=0, callback = earlystopping)
model.save_weight(path)

I used this model for a while, and then I gathered new data and update the model.

model = build()
model.load_weight(path)
model.compile(optimizer=adam, loss='mse')
# update the model
model.fit(X_new, y_new, epochs=1, batch_size=32, verbose=0)

When I update the model, I do not have validation/test data as I wanted to use the most fresh data for time series prediction. To avoid overfitting I pick small epochs varying from 1-3.

But somehow my model performs worse than before. Does anyone have an idea why this is happening? How I should remedy this?

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