Forecasting, Volume 5, Issue 2 (June 2023) – 8 articles

Forecasters have been using various criteria to select the most appropriate model from a pool of candidate models. This includes measurements on the in-sample accuracy of the models, information criteria, and cross-validation, among others. Although the latter two options are generally preferred due to their ability to tackle overfitting, in univariate time-series forecasting settings, limited work has been conducted to confirm their superiority. In this study, we compared such popular criteria for the case of the exponential smoothing family of models using a large data set of real series. Our results suggest that there is significant disagreement between the suggestions of the examined criteria and that, depending on the approach used, models of different complexity may be favored, with possible negative effects on the forecasting accuracy. Moreover, we find that simple in-sample error measures can effectively select forecasting models, especially when focused on the most recent observations in the series. Full article

This study analyzes the transmission of market uncertainty on key European financial markets and the cryptocurrency market over an extended period, encompassing the pre-, during, and post-pandemic periods. Daily financial market indices and price observations are used to assess the forecasting models. We compare statistical, machine learning, and deep learning forecasting models to evaluate the financial markets, such as the ARIMA, hybrid ETS-ANN, and kNN predictive models. The study results indicate that predicting financial market fluctuations is challenging, and the accuracy levels are generally low in several instances. ARIMA and hybrid ETS-ANN models perform better over extended periods compared to the kNN model, with ARIMA being the best-performing model in 2018–2021 and the hybrid ETS-ANN model being the best-performing model in most of the other subperiods. Still, the kNN model outperforms the others in several periods, depending on the observed accuracy measure. Researchers have advocated using parametric and non-parametric modeling combinations to generate better results. In this study, the results suggest that the hybrid ETS-ANN model is the best-performing model despite its moderate level of accuracy. Thus, the hybrid ETS-ANN model is a promising financial time series forecasting approach. The findings offer financial analysts an additional source that can provide valuable insights for investment decisions. Full article

The paper is focused on predicting the financial performance of a small open economy with an automotive industry with an above-standard share. The paper aims to predict the probability distribution of the decomposed relative economic value-added measure of the automotive production sector NACE 29 in the Czech economy. An advanced Monte Carlo simulation prediction model is applied using the exact pyramid decomposition function. The problem is modelled using advanced stochastic process instruments such as Levy-driven mean-reversion, skew t-regression, normal inverse Gaussian distribution, and t-copula interdependencies. The proposed method procedure was found to fit the investigated financial ratios sufficiently, and the estimation was valid. The decomposed approach allows the reflection of the ratios’ complex relationships and improves the prediction results. The decomposed results are compared with the direct prediction. Precision distribution tests confirmed the superiority of the decomposed approach for particular data. Moreover, the Czech automotive sector tends to decrease the mean value and median of financial performance in the future with negative asymmetry and high volatility hidden in financial ratios decomposition. Scholars can generally use forecasting methods to investigate economic system development, and practitioners can obtain quality and valuable information for decision making. Full article

We present a technique to solve the linear integer model with variable bounding. By using the continuous optimal solution of the linear integer model, the variable bounds for the basic variables are approximated and then used to calculate the optimal integer solution. With the variable bounds of the basic variables known, solving a linear integer model is easier by using either the branch and bound, branch and cut, branch and price, branch cut and price, or branch cut and free algorithms. Thus, the search for large numbers of subproblems, which are unnecessary and common for NP Complete linear integer models, is avoided. Full article

Subnational jurisdictions, compared to the apparatuses of countries and large institutions, have less resources and human capital available to carry out an updated conjunctural follow-up of the economy (nowcasting) and for generating economic predictions (forecasting). This paper presents the results of our research aimed at facilitating the economic decision making of regional public agents. On the one hand, we present an interactive app that, based on dynamic factor analysis, simplifies and automates the construction of economic synthetic indicators and, on the other hand, we evaluate how to measure the uncertainty associated with the synthetic indicator. Theoretical and empirical developments show the suitability of the methodology and the approach for measuring and predicting the underlying aggregate evolution of the economy and, given the complexity associated with the dynamic factor analysis methodology, for using bootstrap techniques to measure the error. We also show that, when we combine different economic series by dynamic factor analysis, approximately 1000 resamples is sufficient to properly calculate the confidence intervals of the synthetic index in the different time instants. Full article

Climate change is considered one of the biggest challenges around the globe as it has been causing alterations in hydrological extremes. Climate change and variability have an impact on future streamflow conditions, water quality, and ecological balance, which are further aggravated by anthropogenic activities such as changes in land use. This study intends to provide insight into potential changes in future streamflow conditions leading to changes in flooding patterns. Flooding is an inevitable, frequently occurring natural event that affects the environment and the socio-economic structure of its surroundings. This study evaluates the flooding pattern and inundation mapping of two different rivers, Wabash River in Indiana and Fountain Creek in Colorado, using the observed gage data and different climate models. The Coupled Model Intercomparison Project Phase 6 (CMIP6) streamflow data are considered for the future forecast of the flood. The cumulative distribution function transformation (CDF-t) method is used to correct bias in the CMIP6 streamflow data. The Generalized Extreme Value (L-Moment) method is used for the estimation of the frequency of flooding for 100-year and 500-year return periods. Civil GeoHECRAS is used for each flood event to map flood extent and examine flood patterns. The findings from this study show that there will be a rapid increase in flooding events even in small creeks soon in the upcoming years. This study seeks to assist floodplain managers in strategic planning to adopt state-of-the-art information and provide a sustainable strategy to regions with similar difficulties for floodplain management, to improve socioeconomic life, and to promote environmental sustainability. Full article

Understanding how, why and when energy consumption changes provides a tool for decision makers throughout the power networks. Thus, energy forecasting provides a great service. This research proposes a probabilistic approach to capture the five inherent dimensions of a forecast: three dimensions in space, time and probability. The forecasts are generated through different models based on artificial neural networks as a post-treatment of point forecasts based on shallow artificial neural networks, creating a dynamic ensemble. The singular value decomposition (SVD) technique is then used herein to generate temperature scenarios and project different futures for the probabilistic forecast. In additional to meteorological conditions, time and recency effects were considered as predictor variables. Buildings that are part of a university campus are used as a case study. Though this methodology was applied to energy demand forecasts in buildings alone, it can easily be extended to energy communities as well. Full article

The price of oil is nowadays a hot topic as it affects many areas of the world economy. The price of oil also plays an essential role in how the economic situation is currently developing (such as the COVID-19 pandemic, inflation and others) or the political situation in surrounding countries. The paper aims to predict the oil price movement in stock markets and to what extent the COVID-19 pandemic has affected stock markets. The experiment measures the price of oil from 2000 to 2022. Time-series-smoothing techniques for calculating the results involve multilayer perceptron (MLP) networks and radial basis function (RBF) neural networks. Statistica 13 software, version 13.0 forecasts the oil price movement. MLP networks deliver better performance than RBF networks and are applicable in practice. The results showed that the correlation coefficient values of all neural structures and data sets were higher than 0.973 in all cases, indicating only minimal differences between neural networks. Therefore, we must validate the prediction for the next 20 trading days. After the validation, the first neural network (10 MLP 1-18-1) closest to zero came out as the best. This network should be further trained on more data in the future, to refine the results. Full article