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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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RNNs are known to be superior to MLP in case of sequential data, like yours. But complex models like LSTM and GRU require a lot of data to achieve their potential. I don't know about your data but you can try to validate your architecture, approach and overall setting using a different, known time-series benchmark data. Maybe something is wrong with ...


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Instead of using a token embedding you can use a linear layer. For an input of (10, 5, 4) - (sequence length, batch size, features) you can create a linear layer: self.embedding_layer = nn.Linear(4, d_model) Where d_model is the dimension of the input to the transformer. PositionalEncoding is still needed so as to have a representation of time in the inputs....


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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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