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Normally the method for dealing with 'time series' is really just finding ways to turn a non-stationary distribution into a stationary distribution, where you c

by platz 7y ago

Normally the method for dealing with 'time series' is really just finding ways to turn a non-stationary distribution into a stationary distribution, where you can then apply classic statistical methods on them. So you're just finding ways to factor out the time component in the data so you can use the standard non-time sensitive regression models on the transformed data. I don't think it's untill you get to the NN based models that they start treating time as a first-class component in the model. * If I'm wrong please explain why instead of downvoting

k_f 7y ago
A (weak) stationary distribution has no trend, no seasonality, and no changes in variance. With these properties, predictions are independent of the absolute point in time. The transformations to turn non-stationary series into stationary ones are reversible (usually differencing, log-transformations and alike), thus the predictions can be applied back to the original time series. Treating time as a first-class component really just means to factor in the absolute point in time into the models at training time. This only makes sense if the absolute time changes properties of the distribution that cannot be accounted for with regular transformations. If that's the case, then we assume that these changes cannot be modeled, and are thus either random or follow a complicated systematic we can't grasp. In the first case, a NN wouldn't improve either, in the second case, we either need to always use the full history of the time series to make a prediction, or hope that a complex NN like LSTM might capture the systematic. In any case, I think one of the more compelling reasons to use NN is to not have to do preprocessing. The trade-off is that you end up with a complicated solution compared to the six or so easy-to-understand parameters a SARIMA model might give you. And the latter even might give you some interpretable intuition for the behavior of the process.
jldugger 7y ago
I'm not sure I agree; in a sense the exponential smoothing approaches deliberately factor in time by looking back in time by the seasonality stride. But yes, the reason it's called triple exponential smoothing is that you're repeatedly transforming the data into a form amenable to classical methods. Zero centering and detrending are exactly that of what you speak. But at the end of all this, you still use these components to produce a prediction.
anthony_doan 7y ago
> I don't think it's untill you get to the NN based models that they start treating time as a first-class component in the model. They're bad at prediction for the past several m1-m4 time series tournament for univariate. The best one for m4 is a combination of NN and traditional statistical regression (time series) but it is often deem too tailor to the data. And I don't think differencing out the trend, season, etc... means we're treating the time component as second class. It's just that stationary data is what we know the most currently. There is GARCH/ARCH method too. The nonstationary methods aren't used as often and from the tournaments the current set of time series are the best so far. So I think this comment is misleading. There is also longitudinal analysis, survival analysis, etc... and they all keep time in mind.

by platz 7y ago

k_f 7y ago

A (weak) stationary distribution has no trend, no seasonality, and no changes in variance. With these properties, predictions are independent of the absolute point in time. The transformations to turn non-stationary series into stationary ones are reversible (usually differencing, log-transformations and alike), thus the predictions can be applied back to the original time series. Treating time as a first-class component really just means to factor in the absolute point in time into the models at training time. This only makes sense if the absolute time changes properties of the distribution that cannot be accounted for with regular transformations. If that's the case, then we assume that these changes cannot be modeled, and are thus either random or follow a complicated systematic we can't grasp. In the first case, a NN wouldn't improve either, in the second case, we either need to always use the full history of the time series to make a prediction, or hope that a complex NN like LSTM might capture the systematic. In any case, I think one of the more compelling reasons to use NN is to not have to do preprocessing. The trade-off is that you end up with a complicated solution compared to the six or so easy-to-understand parameters a SARIMA model might give you. And the latter even might give you some interpretable intuition for the behavior of the process.

jldugger 7y ago

I'm not sure I agree; in a sense the exponential smoothing approaches deliberately factor in time by looking back in time by the seasonality stride. But yes, the reason it's called triple exponential smoothing is that you're repeatedly transforming the data into a form amenable to classical methods. Zero centering and detrending are exactly that of what you speak. But at the end of all this, you still use these components to produce a prediction.

anthony_doan 7y ago

> I don't think it's untill you get to the NN based models that they start treating time as a first-class component in the model. They're bad at prediction for the past several m1-m4 time series tournament for univariate. The best one for m4 is a combination of NN and traditional statistical regression (time series) but it is often deem too tailor to the data. And I don't think differencing out the trend, season, etc... means we're treating the time component as second class. It's just that stationary data is what we know the most currently. There is GARCH/ARCH method too. The nonstationary methods aren't used as often and from the tournaments the current set of time series are the best so far. So I think this comment is misleading. There is also longitudinal analysis, survival analysis, etc... and they all keep time in mind.