fdcodepy

fdcodepy

Introduction

• FD_codepy is an open-source python package that can be used to extract time series in an interpretable manner, and use it for compression.

• The key idea is proposed specifically for metered data in energy sector, but can also be used with smart sensors and edge computing.

• Inspired by Codebook method, it breaks down the time series data into its constituent parts, i.e., the unique sub-patterns called Codewords, and the index of the Codewords, i.e., representations, allowing for efficient compression and analysis.

• Compared to resampling data into lower resolution, this lossy compression method takes similar data storage and transmission bandwidth, while preserving high frequency information and accumulative/average metered values.

• The FD_codepy source code is on GitHub: https://github.com/abc123yuanrui/FD_codepy/

  • An example is provided in example/test.ipynb

Key method for time series compression

• Codebook: key class for reconstructing long energy time series into unique partitions (codewords) and representations. Check examples for details.
  • It takes time series, window size, and distance metric types as inputs.
  • Four ensambled distance methods are:
    • Euclidean Distance (default or 'euclidean')
    • Dynamic Time Warping ('DTW')
    • Wasserstein Distance ('Wasserstein')
    • Flexibility Distance ('flexibilityD')
  • preprocessing method will normalise the data into normalised series as attribute normalized_arr, with the scaler attribute scaler_average
  • get_distance_matrix method is a statistical analysis that computes the distance matrix for long time series (assuming we know historical data). It returns the matrix and quantile result for setting a similarity threshold (otherwise, the threshold can be set by an empirical value).
  • desolve_time_series_thre process the time seires into codewords and representations, return them.
  • post_processing reconstructe time series based on codewords and representations, the result stores as attribute recovered_series
• Flexbility distance: a novel distance metric that measures the similarity between time series data while taking into account both temporal and amplitude distance, and the rebound effect of the data.

Installation

• Install using pip: pip install fdcodepy

Usage

• Import the package: import fdcodepy
• Step by step example: Codebook processing for a given hourly time series and window size of 24, which decides the compression ratio (same with resmpling data from hourly into daily)
  • from fdcodepy import methods
  • sample_series = np.random.uniform(0, 30, 365*24)
  • series_codebook = methods.Code_book(time_series, 24, 'flexibilityD')
  • series_codebook.pre_processing()
  • distance_matrix, quantiles = series_codebook.get_distance_matrix()
  • codewords, representations = series_codebook.desolve_time_series_thre(quantiles[0])
  • series_codebook.post_processing()
  • series_codebook.recovered_series is the reconstruted data, computed from the representations with only lenght of len(representations), compared to original series with length len(sample_series)
• The representations are the length of data needs to be communicated to data center, which is equal to the size of downsampled data, in this case, 365
• Use the Codebook processing result to generate a report
  • from fdcodepy.utils.helpers import code_book_processing_analysis
  • code_book_processing_analysis(series_codebook, time_index, report = True, export_dir = '.')
• Use the FlexibilityDistance to compute the flexibility distance between two time series datasets (with default settings).
  • from fdcodepy import methods
  • Code_book.flex_distance(time_series_1, time_series_2)

Example analysis report

The figures can be zoomed to for checking details

Reference

• Yuan, R., Pourmousavi, S. A., Soong, W. L., Black, A. J., Liisberg, J. A. R., & Lemos-Vinasco, J. (2024). Unleashing the benefits of smart grids by overcoming the challenges associated with low-resolution data. Cell Reports Physical Science, 5(2), 101830. https://doi.org/10.1016/j.xcrp.2024.101830
• Yuan, S. A. Pourmousavi, W. L. Soong, A. J. Black, J. A. R. Liisberg, and J. Lemos-Vinasco, “A New Time Series Similarity Measure and Its Smart Grid Applications,” 2023. https://arxiv.org/abs/2310.12399