Implementation to perform forecasting of locally stationary wavelet processes by examining the local second order structure of the time series.

Classical (bottom-up and top-down), optimal combination and heuristic  
    point (Di Fonzo and Girolimetto, 2023 <doi:10.1016/j.ijforecast.2021.08.004>) and 
    probabilistic (Girolimetto et al. 2024 <doi:10.1016/j.ijforecast.2023.10.003>) 
    forecast reconciliation procedures for linearly constrained time series 
    (e.g., hierarchical or grouped time series) in cross-sectional, temporal, 
    or cross-temporal frameworks.

Nonlinear forecast reconciliation with machine learning in 
    cross-sectional (Spiliotis et al. 2021 <doi:10.1016/j.asoc.2021.107756>), 
    temporal, and cross-temporal 
    (Rombouts et al. 2024 <doi:10.1016/j.ijforecast.2024.05.008>) frameworks.

Offers tools for visualizing and analyzing size and power properties
    of tests for equal predictive accuracy, including Diebold-Mariano and
    related procedures. Provides multiple Diebold-Mariano test implementations
    based on fixed-smoothing approaches, including fixed-b methods such as
    Kiefer and Vogelsang (2005) <doi:10.1017/S0266466605050565>, and
    applications to tests for equal predictive accuracy as in Coroneo and
    Iacone (2020) <doi:10.1002/jae.2756>, alongside conventional large-sample
    approximations. HAR inference involves
    nonparametric estimation of the long-run variance, and a key tuning
    parameter (the truncation parameter) trades off size and power. Lazarus,
    Lewis, and Stock (2021) <doi:10.3982/ECTA15404> theoretically characterize
    the size-power frontier for the Gaussian multivariate location model.
    'ForeComp' computes and visualizes the finite-sample size-power frontier of
    the Diebold-Mariano test based on fixed-b asymptotics together with the
    Bartlett kernel. To compute finite-sample size and power, it fits a best
    approximating ARMA process to the input data and reports how the truncation
    parameter performs and how robust testing outcomes are to its choice.