Embedded deep compression for time-series data

A system for compressing time-series data, the system comprising:

• at least one processor;
• and
• at least one memory storing computer-executable instructions, wherein the at least one processor is configured to access the at least one memory and execute the computer-executable instructions to:
• receive input comprising a plurality of prior quantized data values;
• receive learned parameters of one or more prediction models;
• determine, using the one or more prediction models, a predicted current data value based at least in part on the plurality of prior quantized data values and the learned parameters;
• determine a prediction error based at least in part on the predicted current data value and an actual current data value;
• quantize the prediction error to obtain a quantized prediction error;
• determine a quantized current data value based at least in part on the quantized prediction error and the predicted current data value;
• and
• re-learn the parameters based at least in part on the plurality of prior quantized data values and the quantized current data value.

A computer program product for compressing time-series data, the computer program product comprising a storage medium readable by a processing circuit, the storage medium storing instructions executable by the processing circuit to cause a method to be performed, the method comprising:

• receiving input comprising a plurality of prior quantized data values;
• receiving learned parameters of one or more prediction models;
• determining, using the one or more prediction models, a predicted current data value based at least in part on the plurality of prior quantized data values and the learned parameters;
• determining a prediction error based at least in part on the predicted current data value and an actual current data value;
• quantizing the prediction error to obtain a quantized prediction error;
• determining a quantized current data value based at least in part on the quantized prediction error and the predicted current data value;
• and
• re-learning the parameters based at least in part on the plurality of prior quantized data values and the quantized current data value.

A computer-implemented method for decompressing compressed time-series data, the method comprising:

• receiving input comprising a plurality of prior quantized data values;
• receiving learned parameters of one or more prediction models;
• receiving, from a data compressor, a compressed quantized prediction error;
• decompressing the compressed quantized prediction error to obtain a quantized prediction error;
• determining, using the one or more prediction models, a predicted current data value based at least in part on the plurality of prior quantized data values and the learned parameters;
• reconstructing a quantized current data value based at least in part on the quantized prediction error and the predicted current data value;
• and
• re-learning the parameters based at least in part on the plurality of prior quantized data values and the quantized current data value.