The package fnets for the R language implements the suite of methodologies proposed by Barigozzi et al. (2022) for the network estimation and forecasting of high-dimensional time series under a factor-adjusted vector autoregressive model, which permits strong spatial and temporal correlations in the data. Additionally, we provide tools for visualising the networks underlying the time series data after adjusting for the presence of factors. The package also offers data-driven methods for selecting tuning parameters including the number of factors, vector autoregressive order and thresholds for estimating the edge sets of the networks of interest in time series analysis. We demonstrate various features of fnets on simulated datasets as well as real data on electricity prices.

翻译：用于R语言的软件包网将执行Barigozzi等人(2022年)提出的一套方法,用于在按要素调整的矢量自动递减模型下对高维时间序列进行网络估计和预测,该模型使数据具有很强的空间和时间相关性;此外,我们提供工具,在根据存在因素进行调整后,对时间序列数据所依据的网络进行可视化;该软件包还提供数据驱动方法,用于选择调试参数,包括因数、矢量自动递减顺序和在时间序列分析中估算感兴趣的网络边缘数据集的阈值;我们展示模拟数据集中的网形特征以及电价的实际数据。