Embedded deep compression for time-series data

Patent metadata

Field	Value
Publication number	US-2022190842-A1
Application number	US-201917439836-A
Country	US
Kind code	A1
Filing date	Mar 22, 2019
Priority date	Mar 22, 2019
Publication date	Jun 16, 2022
Grant date	—

Abstract

A lossy compression algorithm is described for performing data compression of high-frequency floating point time-series data, for example. The compression algorithm utilizes a prediction engine that employs at least one of a linear prediction model or a non-linear prediction model to calculate one-step-ahead prediction of a current data value at current sampling time t using N previous quantized data values, where N is the model order. A prediction error is determined between the predicted value and an actual value, and the prediction error is quantized. A quantized current data value is determined from the predicted value and the quantized prediction error. The quantized prediction error is sent from an edge device to a data decompressor on a cloud device. The decompressor reconstructs the quantized current data value using the received quantized prediction error and by generating the same predicted value as the compressor.

First claim

What is claimed is:

1 . A computer-implemented method for compressing 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; 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.

2 . The computer-implemented method of claim 1 , further comprising:
compressing the quantized prediction error; and sending the compressed quantized prediction error to a data decompressor that is configured to reconstruct the quantized current data value based at least in part on the compressed quantized prediction error.

3 . The computer-implemented method of claim 1 , wherein determining the prediction error based at least in part on the predicted current data value and the actual current data value comprises calculating, as the prediction error, a difference between the predicted current data value and the actual current data value.

4 . The computer-implemented method of claim 1 , wherein determining the quantized current data value based at least in part on the quantized prediction error and the predicted current data value comprises summing the quantized prediction error and the predicted current data value to obtain the quantized current data value.

5 . The computer-implemented method of claim 1 , wherein determining the predicted current data value based at least in part on the plurality of prior quantized data values and the learned parameters comprises applying, using the learned parameters, at least one of a linear prediction model or a non-linear prediction model to the plurality of prior quantized data values to obtain the predicted current data value.

6 . The computer-implemented method of claim 1 , wherein re-learning the parameters based at least in part on the plurality of prior quantized data values and the quantized current data value comprises minimizing a norm of a plurality of quantized prediction errors.

7 . 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.

8 . The system of claim 7 , wherein the at least one processor is further configured to execute the computer-executable instructions to:
compress the quantized prediction error; and send the compressed quantized prediction error to a data decompressor that is configured to reconstruct the quantized current data value based at least in part on the compressed quantized prediction error.

9 . The system of claim 7 , wherein the at least one processor is configured to determine the prediction error based at least in part on the predicted current data value and the actual current data value by executing the computer-executable instructions to calculate, as the prediction error, a difference between the predicted current data value and the actual current data value.

10 . The system of claim 7 , wherein the at least one processor is configured to determine the quantized current data value based at least in part on the quantized prediction error and the predicted current data value by executing the computer-executable instructions to sum the quantized prediction error and the predicted current data value to obtain the quantized current data value.

11 . The system of claim 7 , wherein the at least one processor is configured to determine the predicted current data value based at least in part on the plurality of prior quantized data values and the learned parameters by executing the computer-executable instructions to apply, using the learned parameters, at least one of a linear prediction model or a non-linear prediction model to the plurality of prior quantized data values to obtain the predicted current data value.

12 . The system of claim 7 , wherein the at least one processor is configured to re-learn the parameters based at least in part on the plurality of prior quantized data values and the quantized current data value by executing the computer-executable instructions to minimize a norm of a plurality of quantized prediction errors.

13 . 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.

14 . The computer program product of claim 13 , the method further comprising:
compressing the quantized prediction error; and sending the compressed quantized prediction error to a data decompressor that is configured to reconstruct the quantized current data value based at least in part on the compressed quantized prediction error.

15 . The computer program product of claim 13 , wherein determining the prediction error based at least in part on the predicted current data value and the actual current data value comprises calculating, as the predicted error, a difference between the predicted current data value and the actual current data value.

16 . The computer program product of claim 13 , wherein determining the quantized current data value based at least in part on the quantized prediction error and the predicted current data value comprises summing the quantized prediction err

Assignees

• Siemens Corp
• Univ Stanford

Inventors

• Wen Chengtao
• Wang Lingyun
• Aparicio Ojea Juan L
• Chandak Shubham
• Shriram Tatwawadi Kedar
• Weissman Tsachy

Classifications

• G06N3/08 Primary

  Learning methods · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/09

  Supervised learning · CPC title

• H03M7/3059 Primary

  Digital compression and data reduction techniques where the original information is represented by a subset or similar information, e.g. lossy compression · CPC title