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Model for classifying time-series data with distinct features?

model for univariate time series with 0,1 as data valuesTime series with erroneous dataClassifying time series data that overlapFeatures for blink detection in real-time single channel EEGHow can I prepare my data from multiple time series sources for time series regression?Normalising time(minutes) related data with n other input variables(also dependant on time)Input shape for simpler time series in LSTM+CNNMultivariate time series forecasting with LSTMTime Series prediction for uneven data with some data providedTrain LSTM model with multiple time series

0

$begingroup$

I've heard about time-series classification being done with TCN's and CNN's combined with LSTM's very often, citing that CNN's would provide insight both forward and in the past since you already have all the information for that time period. For my application, there is a distinct shape and I'd like to classify whether it exists or not. For example, I want to detect whether the data looks like this or this

Of course, there would be noise involved and the feature would be much less obvious making the problem worthy of using machine learning. Is there some way I can exploit this knowledge of there being a single important feature (this hump) to use a different architecture or do anything differently? Thanks.

time-series lstm cnn

share|improve this question

asked Mar 19 at 3:33

Rithwik SudharsanRithwik Sudharsan

1

New contributor

Rithwik Sudharsan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Does this problem have only 1 input variable ?
  $endgroup$
  – Shamit Verma
  Mar 19 at 6:58
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Yes, as a function of time, but I would think the answer would apply to more complex problems?
  $endgroup$
  – Rithwik Sudharsan
  Mar 19 at 20:04
  

  
  
  
  

add a comment | 

0

$begingroup$

I've heard about time-series classification being done with TCN's and CNN's combined with LSTM's very often, citing that CNN's would provide insight both forward and in the past since you already have all the information for that time period. For my application, there is a distinct shape and I'd like to classify whether it exists or not. For example, I want to detect whether the data looks like this or this

Of course, there would be noise involved and the feature would be much less obvious making the problem worthy of using machine learning. Is there some way I can exploit this knowledge of there being a single important feature (this hump) to use a different architecture or do anything differently? Thanks.

time-series lstm cnn

share|improve this question

asked Mar 19 at 3:33

Rithwik SudharsanRithwik Sudharsan

1

New contributor

Rithwik Sudharsan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Does this problem have only 1 input variable ?
  $endgroup$
  – Shamit Verma
  Mar 19 at 6:58
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Yes, as a function of time, but I would think the answer would apply to more complex problems?
  $endgroup$
  – Rithwik Sudharsan
  Mar 19 at 20:04
  

  
  
  
  

add a comment | 

$begingroup$

I've heard about time-series classification being done with TCN's and CNN's combined with LSTM's very often, citing that CNN's would provide insight both forward and in the past since you already have all the information for that time period. For my application, there is a distinct shape and I'd like to classify whether it exists or not. For example, I want to detect whether the data looks like this or this

Of course, there would be noise involved and the feature would be much less obvious making the problem worthy of using machine learning. Is there some way I can exploit this knowledge of there being a single important feature (this hump) to use a different architecture or do anything differently? Thanks.

time-series lstm cnn

share|improve this question

asked Mar 19 at 3:33

Rithwik SudharsanRithwik Sudharsan

1

New contributor

Rithwik Sudharsan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

share|improve this question

asked Mar 19 at 3:33

Rithwik SudharsanRithwik Sudharsan

1

New contributor

Rithwik Sudharsan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

share|improve this question

asked Mar 19 at 3:33

Rithwik SudharsanRithwik Sudharsan

1

New contributor

Rithwik Sudharsan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked Mar 19 at 3:33

Rithwik SudharsanRithwik Sudharsan

1

New contributor

Rithwik Sudharsan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked Mar 19 at 3:33

Rithwik SudharsanRithwik Sudharsan

1

1 Answer
1

active

oldest

votes

0

$begingroup$

This specific problem looks at the pattern across the whole data I.e. pattern will not show up from time < -3 or time > 3 for a given curvature.

You can try two models :

1. Simple feed-forward Network with number of inputs = number of time steps (Maybe scale / shift the data so that it always has the same number of time steps )

This should be able to detect some patterns for classification (Like f(0) must be less that f(4))

2. Univariate LSTM with different sizes of time steps in each sample

This should be able to learn that f(x) should stay near constant, reduce and then increase and return to constant

Both networks will have a sigmoid in output layer since it is a binary classification problem.

Code exmaple for LSTM : https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/

share|improve this answer

answered Mar 20 at 4:10

Shamit VermaShamit Verma

90929

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  How exactly would this take into account the existence of that central feature? Sorry I don't see exactly where this would be different from any standard time classification approach
  $endgroup$
  – Rithwik Sudharsan
  yesterday
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Time classification works well with patterns that repeat (Say f(x) dips below 0 every N steps). This patterns happens only once. So , network have to learn parts of the pattern.
  $endgroup$
  – Shamit Verma
  yesterday
  

  
  
  
  

add a comment | 

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0

$begingroup$

This specific problem looks at the pattern across the whole data I.e. pattern will not show up from time < -3 or time > 3 for a given curvature.

You can try two models :

1. Simple feed-forward Network with number of inputs = number of time steps (Maybe scale / shift the data so that it always has the same number of time steps )

This should be able to detect some patterns for classification (Like f(0) must be less that f(4))

2. Univariate LSTM with different sizes of time steps in each sample

This should be able to learn that f(x) should stay near constant, reduce and then increase and return to constant

Both networks will have a sigmoid in output layer since it is a binary classification problem.

Code exmaple for LSTM : https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/

share|improve this answer

answered Mar 20 at 4:10

Shamit VermaShamit Verma

90929

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  How exactly would this take into account the existence of that central feature? Sorry I don't see exactly where this would be different from any standard time classification approach
  $endgroup$
  – Rithwik Sudharsan
  yesterday
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Time classification works well with patterns that repeat (Say f(x) dips below 0 every N steps). This patterns happens only once. So , network have to learn parts of the pattern.
  $endgroup$
  – Shamit Verma
  yesterday
  

  
  
  
  

add a comment | 

0

$begingroup$

This specific problem looks at the pattern across the whole data I.e. pattern will not show up from time < -3 or time > 3 for a given curvature.

You can try two models :

1. Simple feed-forward Network with number of inputs = number of time steps (Maybe scale / shift the data so that it always has the same number of time steps )

This should be able to detect some patterns for classification (Like f(0) must be less that f(4))

2. Univariate LSTM with different sizes of time steps in each sample

This should be able to learn that f(x) should stay near constant, reduce and then increase and return to constant

Both networks will have a sigmoid in output layer since it is a binary classification problem.

Code exmaple for LSTM : https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/

share|improve this answer

answered Mar 20 at 4:10

Shamit VermaShamit Verma

90929

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  How exactly would this take into account the existence of that central feature? Sorry I don't see exactly where this would be different from any standard time classification approach
  $endgroup$
  – Rithwik Sudharsan
  yesterday
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Time classification works well with patterns that repeat (Say f(x) dips below 0 every N steps). This patterns happens only once. So , network have to learn parts of the pattern.
  $endgroup$
  – Shamit Verma
  yesterday
  

  
  
  
  

add a comment | 

$begingroup$

This specific problem looks at the pattern across the whole data I.e. pattern will not show up from time < -3 or time > 3 for a given curvature.

You can try two models :

1. Simple feed-forward Network with number of inputs = number of time steps (Maybe scale / shift the data so that it always has the same number of time steps )

This should be able to detect some patterns for classification (Like f(0) must be less that f(4))

2. Univariate LSTM with different sizes of time steps in each sample

This should be able to learn that f(x) should stay near constant, reduce and then increase and return to constant

Both networks will have a sigmoid in output layer since it is a binary classification problem.

Code exmaple for LSTM : https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/

share|improve this answer

answered Mar 20 at 4:10

Shamit VermaShamit Verma

90929

$endgroup$

share|improve this answer

answered Mar 20 at 4:10

Shamit VermaShamit Verma

90929

answered Mar 20 at 4:10

Shamit VermaShamit Verma

90929

answered Mar 20 at 4:10

Shamit VermaShamit Verma

90929