Traffic Weaver: semi-synthetic time-varying traffic generator based on averaged time series

TL;DR

Traffic Weaver is a Python tool that generates detailed semi-synthetic time series traffic data closely matching original signals, aiding in network model development and validation.

Contribution

It introduces a novel Python package for creating semi-synthetic, fine-grained, time-varying traffic signals based on averaged time series data.

Findings

Produces signals that closely match original data upon averaging

Enables detailed traffic simulation for network optimization

Supports development of traffic prediction models

Abstract

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, and 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.