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We analyse multimodal time-series data corresponding to weight, sleep andsteps measurements. We focus on predicting whether a user will successfullyachieve his/her weight objective. For this, we design several deep longshort-term memory (LSTM) architectures, including a novel cross-modal LSTM(X-LSTM), and demonstrate their superiority over baseline approaches. TheX-LSTM improves parameter efficiency by processing each modality separately andallowing for information flow between them by way of recurrentcross-connections. We present a general hyperparameter optimisation techniquefor X-LSTMs, which allows us to significantly improve on the LSTM and a priorstate-of-the-art cross-modal approach, using a comparable number of parameters.Finally, we visualise the model's predictions, revealing implications aboutlatent variables in this task.

Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data