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You want to build a Stacked LSTM network with multiple features ( what you name parameters is often called features ), this is described in https://machinelearningmastery.com/stacked-long-short-term-memory-networks/ and https://machinelearningmastery.com/use-features-lstm-networks-time-series-forecasting/ and https://datascience.stackexchange.com/questions/17024/rnns-with-multiple-features

RNNs and so LSTMs are only able to handle sequential data, however this can be expanded by a feature vector with more than one dimensions ( your ensemble of parameters as described in the answer in https://datascience.stackexchange.com/questions/17024/rnns-with-multiple-features )

The displayed structure of the 6 LSTM cells in 2 layers is a Stacked LSTM network with 2 layers feature_dim = data_dim=6 (or 7) ( number of your parameters / features ) and timesteps=3 ( 2 layers with 3 unit in each layer ) cf section Stacked LSTM for sequence classification in https://keras.io/getting-started/sequential-model-guide/ and How to stack multiple lstm in keras? for keras code.

Setting the accurate input shape is vital cf Understanding Keras LSTMs, your network is many-to-many case. The shape of the input passed to the LSTM should be in the form (num_samples,timesteps,data_dim) where data_dim is the feature vector or vector of your parameters

Embedding Layers are for One-Hot encoding cf https://towardsdatascience.com/deep-learning-4-embedding-layers-f9a02d55ac12 for keras code see https://towardsdatascience.com/deep-learning-4-embedding-layers-f9a02d55ac12 and https://keras.io/layers/embeddings/ , possibly you could also use simple label encoding ( http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html , http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder )