COMPARING LSTM AND ARIMA FOR FORECASTING (WHEN TO USE WHAT?)
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ARIMA models are relatively simple and can be implemented using basic statistical software, while LSTM models are more complex and require specialized deep learning libraries and hardware. ARIMA models are relatively easy to interpret, as they are based on statistical theory and can be analysed using standard techniques. LSTM models, on the other hand, are more difficult to interpret, as they are based on complex neural network architectures and involve many parameters.
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