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.

翻译：交通编织器（Traffic Weaver）是一个Python软件包，用于基于平均时间序列生成粒度更细的半合成信号（时间序列），且该信号在再次平均后能与原始输入信号高度吻合。生成信号的关键组件包括：采用特定策略进行过采样、拉伸以匹配原始时间序列的积分值、平滑处理、重复操作、施加趋势项以及添加噪声。开发Traffic Weaver的主要目的是为电信网络中的时变流量提供半合成的流量数据，从而促进流量预测模型的开发与验证，并辅助部署针对时变流量优化的网络算法。