Hello there, I am trying to build a TCN model to predict a continuous variable. I have time series data in which I am using 3 input features (accelrometer measuments in x,y,z directions) to estimate/predict a continuous variable. I have acceleromter data from 10 different trials stored in a 10×1 cell and each cell has the three accelerometer measurments over time stored in a 500×3 table for that trial. The target continous varable I am trying to predict is simialrly stored in a 10×1 cell array with each cell contaning a the a 500×1 table which is the true value of the predicted variable over time named "Taget". If I am trying to build a TCN model with this data what is the best way to partition the data for training, testing (10%), and validation (10%)? I think I need to use the tspartition function but am not sure how to use it for this type of data. Do I need to combine the data from all 10 trials into one large table and then partition? Or should I partition each trial seprately, train the model on a singluar trial, and then retrain the model on the next trial and so on. Any help would be greatly appreciated!Hello there, I am trying to build a TCN model to predict a continuous variable. I have time series data in which I am using 3 input features (accelrometer measuments in x,y,z directions) to estimate/predict a continuous variable. I have acceleromter data from 10 different trials stored in a 10×1 cell and each cell has the three accelerometer measurments over time stored in a 500×3 table for that trial. The target continous varable I am trying to predict is simialrly stored in a 10×1 cell array with each cell contaning a the a 500×1 table which is the true value of the predicted variable over time named "Taget". If I am trying to build a TCN model with this data what is the best way to partition the data for training, testing (10%), and validation (10%)? I think I need to use the tspartition function but am not sure how to use it for this type of data. Do I need to combine the data from all 10 trials into one large table and then partition? Or should I partition each trial seprately, train the model on a singluar trial, and then retrain the model on the next trial and so on. Any help would be greatly appreciated! Hello there, I am trying to build a TCN model to predict a continuous variable. I have time series data in which I am using 3 input features (accelrometer measuments in x,y,z directions) to estimate/predict a continuous variable. I have acceleromter data from 10 different trials stored in a 10×1 cell and each cell has the three accelerometer measurments over time stored in a 500×3 table for that trial. The target continous varable I am trying to predict is simialrly stored in a 10×1 cell array with each cell contaning a the a 500×1 table which is the true value of the predicted variable over time named "Taget". If I am trying to build a TCN model with this data what is the best way to partition the data for training, testing (10%), and validation (10%)? I think I need to use the tspartition function but am not sure how to use it for this type of data. Do I need to combine the data from all 10 trials into one large table and then partition? Or should I partition each trial seprately, train the model on a singluar trial, and then retrain the model on the next trial and so on. Any help would be greatly appreciated! model, machine learning MATLAB Answers — New Questions

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