Add prediction framework on perma features

[mo12896]

It should at least perform better than naive guessing, using the average over all PERMA pillars. (Good proxy for bayes error, since we do not have a human-level evaluation)

[mo12896]

Model the problem as a multivariate regression task, and then - eventually - set thresholds to get PERMA "classes".

[mo12896]

For time series data:

Dealing with multiple zero values in time series data when using a Recurrent Neural Network (RNN) can be challenging. Here are some strategies that you can try:

Imputation: You can impute the missing values with some reasonable value such as the mean, median, or the last non-zero value. This approach can help preserve the time-series structure and may work well if the missing values are intermittent.

Interpolation: You can interpolate the missing values based on the surrounding non-zero values. This approach can be useful if the missing values occur in a sequence and can be predicted based on the surrounding values.

Data Augmentation: You can use data augmentation techniques to generate synthetic data to fill in the missing values. For example, you can use data augmentation techniques like linear interpolation, random walk, or time-series forecasting to generate synthetic data to fill in the missing values.

Ignore: Depending on the specific problem you are trying to solve, you may be able to ignore the missing values and use the available data to train the RNN.

Masking: Many deep learning frameworks provide masking support for dealing with missing values in time series data. You can use a mask to indicate the locations of missing values, and the RNN will ignore those locations during training and prediction.

Model Selection: Depending on the extent of missing values in your time series data, it may be necessary to select a different model architecture that is better suited to handle missing values, such as a Convolutional Neural Network (CNN) or an Autoencoder.

Ultimately, the best approach for dealing with multiple zero values in time series data when using an RNN will depend on the specific problem you are trying to solve and the characteristics of your data.