Tensorized lstm with adaptive shared memory for learning trends in multivariate time series

A non-transitory computer-readable storage medium comprising a computer-readable program for executing a multi-task deep learning model for learning trends in multivariate time series, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of:

• collecting multi-variate time series data from a plurality of sensors;
• jointly learning both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends;
• and
• employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.

A system for executing a multi-task deep learning model for learning trends in multivariate time series, the system comprising:

• a memory;
• and
• one or more processors in communication with the memory configured to:
• collect multi-variate time series data from a plurality of sensors;
• jointly learn both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends;
• and
• employ a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.