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

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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













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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 enter image description here or this enter image description here



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.










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    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 enter image description here or this enter image description here



    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.










    share|improve this question







    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.







    $endgroup$














      0












      0








      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 enter image description here or this enter image description here



      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.










      share|improve this question







      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.







      $endgroup$




      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 enter image description here or this enter image description here



      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






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      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







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      Rithwik Sudharsan is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      asked 13 mins ago









      Rithwik SudharsanRithwik Sudharsan

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