Convenient functions for ensemble forecasts in R combining
    approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(),
    thetaf(), nnetar(), stlm(), tbats(), and snaive() can be combined with equal weights, weights
    based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>),
    or cross-validated weights. Cross validation for time series data with user-supplied models
    and forecasting functions is also supported to evaluate model accuracy.

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

The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.

Methods to compute linear h-step ahead prediction coefficients based
    on localised and iterated Yule-Walker estimates and empirical mean squared
    and absolute prediction errors for the resulting predictors. Also, functions
    to compute autocovariances for AR(p) processes, to simulate tvARMA(p,q) time
    series, and to verify an assumption from Kley et al. (2019), Electronic of Statistics,
    forthcoming. Preprint <arXiv:1611.04460>.

Provides a test bench for the comparison of forecasting
    methods in uni-variate time series. Forecasting methods are compared using 
    different error metrics. Proposed forecasting methods and alternative error 
    metrics can be used. Detailed discussion is provided in the vignette.

Classical (bottom-up and top-down), optimal and heuristic combination forecast 
    point (Di Fonzo and Girolimetto, 2023) <doi:10.1016/j.ijforecast.2021.08.004> and 
    probabilistic (Girolimetto et al. 2023) <arXiv:2303.17277> reconciliation procedures 
    for cross-sectional, temporal, and cross-temporal linearly constrained time series.