Forecasting Combination of Hierarchical Time Series: A Novel Method with an Application to CoVid-19

Multiple, hierarchically organized time series are routinely submitted to the forecaster upon request to provide estimates of their future values, regardless the level occupied in the hierarchy. In this paper, a novel method for the prediction of hierarchically structured time series will be presented. The idea is to enhance the quality of the predictions obtained using a technique of the type forecast reconciliation, by applying this procedure to a set of optimally combined predictions, generated by different statistical models. The goodness of the proposed method will be evaluated using the official time series related to the number of people tested positive to the SARS-CoV-2 in each of the Italian regions, between February 24th 2020 and August 31th 2020. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Forecasting United Kingdom's energy consumption using machine learning and hybrid approaches

Investigating the current and future dynamics of energy consumption in modern economies such as the UK is crucial. This paper predicts the UK's energy consumption using data spanning January 1995 to March 2022 by comparing and evaluating the forecast performance of machine learning, dynamic regression, time series and combination modelling techniques. The analysis reveals that the seasonal ARIMA and TBATS hybrid models yield the lowest forecast errors in predicting the UK's electricity and gas consumption. Although the combination forecasts performed poorly relative to other models, machine learning techniques such as neural network and support vector regression produced better results compared to the dynamic regression models, whereas the seasonal hybrid model performed better than the machine learning and time series models. The results indicate that the UK's electricity consumption would either stabilise or decline over the forecast horizon, suggesting that it will take some years for electricity consumption to attain pre-2019 levels. For gas consumption, the results indicate that consumption would either maintain current levels or increase over the forecast period. We also show that combination forecasts do not often generate the best predictions, and therefore, choice of methodology matters in energy consumption forecasting. Overall, changing seasonal patterns, energy efficiency improvements, shift to renewable sources and uncertainties due to the COVID-19 pandemic, Brexit, and the Russia–Ukraine crisis appear to be significant drivers of energy consumption in the UK in recent times. These findings are expected to help in designing more effective energy policies as well as guide investor decisions in the energy sector. © The Author(s) 2022.