Ways to normalize multiple timeseries samples for shallow/deep learning?
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Let's say I have many samples that are timeseries, giving me data with shape (samples, timesteps, features). What would be the correct way of normalizing them, if the raw values can vary by orders of magnitude due to e.g. different instruments which output data in various scales (such as recording with different bit depths).
For deep learning, I guess it would make sense just to normalize each individual sample to [0,1] (or [-1,1] if timeseries is supposed to be stationary?), given that that the sample has a natural min/max, and that that with batch normalization and activations, the network doesn't care.
But for standard classifiers, if datapoints are shuffled and time dependence removed, which way makes the most sense? Reshaping to (samples, features) and standardizing? Or normalizing each individual sample, then reshaping, thus removing the need for a scaler altogether?
I guess having data raw data in [0, 2^8] and [0, 2^16], then normalizing them together would wreck different amounts of havoc on various algorithms, because they violate some underlying assumption about uniform/normal distribution of features?
time-series normalization
asked 2 days ago
komodovaran_komodovaran_
1113
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add a comment |
$begingroup$
Let's say I have many samples that are timeseries, giving me data with shape (samples, timesteps, features). What would be the correct way of normalizing them, if the raw values can vary by orders of magnitude due to e.g. different instruments which output data in various scales (such as recording with different bit depths).
For deep learning, I guess it would make sense just to normalize each individual sample to [0,1] (or [-1,1] if timeseries is supposed to be stationary?), given that that the sample has a natural min/max, and that that with batch normalization and activations, the network doesn't care.
But for standard classifiers, if datapoints are shuffled and time dependence removed, which way makes the most sense? Reshaping to (samples, features) and standardizing? Or normalizing each individual sample, then reshaping, thus removing the need for a scaler altogether?
I guess having data raw data in [0, 2^8] and [0, 2^16], then normalizing them together would wreck different amounts of havoc on various algorithms, because they violate some underlying assumption about uniform/normal distribution of features?
time-series normalization
asked 2 days ago
komodovaran_komodovaran_
1113
$endgroup$
add a comment |
$begingroup$
Let's say I have many samples that are timeseries, giving me data with shape (samples, timesteps, features). What would be the correct way of normalizing them, if the raw values can vary by orders of magnitude due to e.g. different instruments which output data in various scales (such as recording with different bit depths).
For deep learning, I guess it would make sense just to normalize each individual sample to [0,1] (or [-1,1] if timeseries is supposed to be stationary?), given that that the sample has a natural min/max, and that that with batch normalization and activations, the network doesn't care.
But for standard classifiers, if datapoints are shuffled and time dependence removed, which way makes the most sense? Reshaping to (samples, features) and standardizing? Or normalizing each individual sample, then reshaping, thus removing the need for a scaler altogether?
I guess having data raw data in [0, 2^8] and [0, 2^16], then normalizing them together would wreck different amounts of havoc on various algorithms, because they violate some underlying assumption about uniform/normal distribution of features?
time-series normalization
asked 2 days ago
komodovaran_komodovaran_
1113
$endgroup$
Let's say I have many samples that are timeseries, giving me data with shape (samples, timesteps, features). What would be the correct way of normalizing them, if the raw values can vary by orders of magnitude due to e.g. different instruments which output data in various scales (such as recording with different bit depths).
For deep learning, I guess it would make sense just to normalize each individual sample to [0,1] (or [-1,1] if timeseries is supposed to be stationary?), given that that the sample has a natural min/max, and that that with batch normalization and activations, the network doesn't care.
But for standard classifiers, if datapoints are shuffled and time dependence removed, which way makes the most sense? Reshaping to (samples, features) and standardizing? Or normalizing each individual sample, then reshaping, thus removing the need for a scaler altogether?
I guess having data raw data in [0, 2^8] and [0, 2^16], then normalizing them together would wreck different amounts of havoc on various algorithms, because they violate some underlying assumption about uniform/normal distribution of features?
time-series normalization
time-series normalization
asked 2 days ago
komodovaran_komodovaran_
1113
asked 2 days ago
komodovaran_komodovaran_
1113
asked 2 days ago
komodovaran_komodovaran_
1113
asked 2 days ago
komodovaran_komodovaran_
1113
asked 2 days ago
komodovaran_komodovaran_
1113
1113
add a comment |
add a comment |
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