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Lightweight Online Imputation of Sensor Data Streams via Temporal Feature Propagation

This is the repository of TFP project which consists of code, datasets, instructions for reproducibility.

Implementation Details

Requirements

  • Pytorch 1.8.1
  • Numpy 1.19.2
  • Pandas 1.1.3
  • Sklearn 0.24.1
  • pypots
  • torch_geometric
  • confluent-kafka 2.2.0
  • codecarbon 2.3.4

You may use " pip3 install -r requirements.txt" to install the above python libraries. Notably, Docker is in need to help create Kafka data streams.

Usage

Produce a sensor data stream:

cd kafka; 
docker compose up --build; # start Kafka engine
python3 produceKafkaStream.py --dataset Motion --miss_ratio 0.9 # produce a sensor data stream with specified sparsity levels

Impute a sensor data stream:

wTFP/wTFPd

python3 imputeStream_wTFP.py --p 10 --tau 5;

baselines

cd baseline;
python3 imputeStream_CD.py --p 10; #CD
python3 imputeStream_mpin.py --p 10; #mpin
python3 imputeStream_MICE.py --p 10; #MICE
python3 imputeStream_MF.py --p 10; #MF
....

The performance results are stored in the default folder: ./exp_results/. For method-specific parameters (e.g., K value for KNN), they are already tuned properly. However, they can be changed as well from the script entrance if needs arise.

Close the sensor data stream:

cd kafka; 
docker compose down; # close Kafka engine

Explaination of Parameters

  • dataset: sensor data stream dataset, e.g., Motion, Water, and Gas
  • miss_ratio: the sparsity level of sensor data stream, e.g., 0.9
  • tau: retrospect length
  • p: the number of incremental/new instances

Acknowledgements

We appreciate the work of SAITS, and their contributed codes available in here. We are also grateful for the repository here to facilitate the creation of sensor data streams using Kafka.