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You already figured out much of the problem. You can solve it with sequence models like LSTM/GRU. One-hot encode word-types. Assume there are types of [properNoun, adjective, noun] as you said. Then "Mike" will be represented as a vector, [1,0,0], "fast" as [0,1,0], and "airplane" as [0,0,1]. Summing up these, you will train a ...


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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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You can claim to use a real-world dataset, you would just need to specify that some values were interpolated. Do you have to have the inter-mediate values though? By the looks of it, each "region" was only measured every 2 hours, so I would just keep it that way and just have the resolution be 2 hours. It doesn't have to be hourly, and probably ...


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