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I don't think you need to go for aggregation -- this looks like a job for VARIMA, the vector-version of ARIMA. In ARIMA, the output of the sequence at time $t$, which can be notated $X_t$, is a function of the past inputs $\{X_1, X_2, \dots, X_{t-1}\}$. For a univariate $AR(k)$ process, the corresponding ARIMA model is given by $$ X_t - \sum_{i=1}^k \alpha_i ...


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I have found nice tutorial in the Tensorflow documentation: https://www.tensorflow.org/tutorials/structured_data/time_series They implement and test both strategies. In the first case, for multi dimensional time series, they output the vector of dimension out_steps * series_dim and then reshape to (out_steps, series dim) They create a model (AR LSTM), that ...


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Atomwise an ai startup uses a 3d convolution neural network to predict if a molecule will bind to an protein. The covid protein had several human attempts to find a molecule to bind. see on young inventors approach to solving covid https://www.cnn.com/2020/10/18/us/anika-chebrolu-covid-treatment-award-scn-trnd/index.html Anika's winning invention uses ...


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