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SrPinko t1_iyjsraw wrote

I agree, for univariate timer series an statistical model should be enough in the most of the cases; however, I still thinks that DL models would outperform statistical models in multivariate time series with a big set of variables, like the MIMIC-III database. Am I wrong with this belief?

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mtocrat t1_iyk1se1 wrote

Even for univariate time series, when you have the data & complexity, DL will obviously outperform simple methods. Show me the simple statistical method that can generate speech, a univariate time-series.

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TrueBirch t1_iymf42w wrote

Wouldn't a DL model trained on a waveform just assume you were going to keep repeating the same words over and over?

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mtocrat t1_iymi8i7 wrote

You could already tape together a deep learning solution consisting of neural speech recognition, an LLM and Wavenet. Counts as a deep learning solution in my book. I'm not sure if anyone has built an end-to-end solution and I expect it would be worse, but I'm sure if someone put their mind and money to it you'd get decent results

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kraegarthegreat t1_iyorlrr wrote

From my personal experience:

- Univariate with a few timesteps: XGBoost or statistical methods.

- Multivariate with many timesteps: NN-based models.

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TrueBirch t1_iymf0eo wrote

Depends how much data you have and how much signal there is. Separating signal from noise in a high-dimensional time series is always a challenge.

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