fnets: An R Package for Network Estimation and Forecasting via Factor-Adjusted VAR Modelling

TL;DR

The paper introduces the R package fnets, which implements methodologies for network estimation and forecasting in high-dimensional time series using factor-adjusted VAR models, with tools for visualization and parameter tuning.

Contribution

It provides an R package that operationalizes advanced factor-adjusted VAR methodologies for network analysis and forecasting in high-dimensional time series data.

Findings

Effective network estimation demonstrated on simulated data

Successful application to electricity price data

Tools for parameter selection and visualization included

Abstract

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.