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：R语言包fnets实现了Barigozzi等人（2022）提出的方法体系，用于在因子调整向量自回归模型下对高维时间序列进行网络估计与预测，该模型允许数据中存在强空间和时间相关性。此外，我们提供了在调整因子存在后进行时间序列数据底层网络可视化的工具。该包还提供数据驱动的调参选择方法，包括因子数量、向量自回归阶数以及用于估计时间序列分析中目标网络边集的阈值。我们通过模拟数据集以及电价真实数据展示了fnets的各项功能。