Traffic Weaver

Semi-synthetic time-varrying traffic generator based on averaged time series.

Acknowledgments and citation

TBD

Table of content

• Documentation
  • Introduction
  • Quick start
  • API reference

Introduction

Traffic Weaver is a Python package developed to generate a semi-synthetic signal (time series) with finer granularity, based on averaged time series, in a manner that, upon averaging, closely matches the original signal provided. The key components utilized to recreate the signal encompass:

• oversampling with a given strategy,
• stretching to match the integral of the original time series,
• smoothing,
• repeating,
• applying trend,
• adding noise.

The primary motivation behind Traffic Weaver is to furnish semi-synthetic time-varying traffic in telecommunication networks, facilitating the development and validation of traffic prediction models, as well as aiding in the deployment of network optimization algorithms tailored for time-varying traffic.

Below figure shows a general usage example. Based on the provided original averaged time series (a), the signal is $n$-times oversampled with a predefined strategy (b). Next, it is stretched to match the integral of the input time series function (c). Further, it is smoothed with a spline function (d). In order to create weekly semi-synthetic data, the signal is repeated seven times (e), applying a long-term trend consisting of sinusoidal and linear functions (f). Finally, the noise is introduced to the signal, starting from small values and increasing over time (g). To validate the correctness of the applied processing, (h) presents the averaged two periods of the created signal, showing that they closely match the original signal (except the applied trend).