Forecasting, Volume 4, Issue 3 (September 2022) – 9 articles

The techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two promising approaches, this paper focuses on the utility of the Exponential Smoothing-Recurrent Neural Network (ES-RNN) in the pool of base learners for different ensembles. We compare against some state-of-the-art ensembling techniques and arithmetic model averaging as a benchmark. We experiment with the M4 forecasting dataset of 100,000 time-series, and the results show that the Feature-Based FORecast Model Averaging (FFORMA), on average, is the best technique for late data fusion with the ES-RNN. However, considering the M4’s Daily subset of data, stacking was the only successful ensemble at dealing with the case where all base learner performances were similar. Our experimental results indicate that we attain state-of-the-art forecasting results compared to Neural Basis Expansion Analysis (N-BEATS) as a benchmark. We conclude that model averaging is a more robust ensembling technique than model selection and stacking strategies. Further, the results show that gradient boosting is superior for implementing ensemble learning strategies. Full article

Forecasting the state of an economy is important for policy makers and business leaders. When this is conducted in real-time, it is called nowcasting. In this paper, we present a method that shows how forecasting errors decline as additional contemporaneous information unfolds and becomes available. When the economic environment changes fast, as has happened often in the last decades across most developed economies, it is important to use forecasting methods that are both flexible and robust. This can be achieved with bridge equations and non-parametric estimates of the trend growth using only publicly available information. The method presented in this paper achieves, by the end of a quarter, an accuracy that is equivalent to the methods used by official entities. Full article

Economic and financial forecasts are important for business planning and government policy but are notoriously challenging. We take advantage of recent advances in individual and group judgement, and a data set of economic and financial forecasts compiled over 25 years, consisting of multiple individual and institutional estimates, to test the claim that nominal groups will make more accurate economic and financial forecast than individuals. We validate the forecasts using the subsequent published (real) outcomes, explore the performance of nominal groups against institutions, identify potential superforecasters and discuss the benefits of implementing structured judgment techniques to improve economic and financial forecasts. Full article

In this research, we investigate the relationship between a movie’s gross and its budget, year of release, season of release, genre, and rating. The movie data used in this research are severely skewed to the right, resulting in the problems of nonlinearity, non-normal distribution, and non-constant variance of the error terms. To overcome these difficulties, we employ a Gaussian copula marginal regression (GCMR) model after adjusting the gross and budget variables for inflation using a consumer price index. An analysis of the data found that year of release, budget, season of release, genre, and rating were all statistically significant predictors of movie gross. Specifically, one unit increases in budget and year were associated with an increase in movie gross. G movies were found to gross more than all other kinds of movies (PG, PG-13, R, and Other). Movies released in the fall were found to gross the least compared to the other three seasons. Finally, action movies were found to gross more than biography, comedy, crime, and other movie genres, but gross less than adventure, animation, drama, fantasy, horror, and mystery movies. Full article

Tourism plays an important economic role for many economies and after the COVID-19 pandemic, accurate tourism forecasting become critical for policymakers in tourism-dependent economies. This paper extends the growing literature on the use of internet search data in tourism forecasting through evaluating the predictive ability of Destination Insight with Google, a new Google product designed to monitor tourism recovery after the COVID-19 pandemic. This paper is the first attempt to explore the forecasting ability of the new Google data. The study focuses on the case of Dubai, given its status as a world-leading tourism destination. The study uses time series models that account for seasonality, trending variables, and structural breaks. The study uses monthly data for the period of January 2019 to April 2022. We explore whether the internet travel search queries can improve the forecasting of tourist arrivals to Dubai from the UK. We evaluate the accuracy of forecasts after incorporating the Google variable in our model. Our findings suggest that the new Google data can significantly improve tourism forecasting and serves as a leading indicator of tourism demand. Full article

Ever since the start of the coronavirus pandemic, lockdowns to curb the spread of the virus have resulted in an increased interest of retail investors in the stock market, due to more free time, capital, and commission-free trading brokerages. This interest culminated in the January 2021 short squeeze wave, caused in no small part due to the coordinated trading moves of the r/WallStreetBets subreddit, which has rapidly grown in user base since the event. In this paper, we attempt to discover if coordinated trading by retail investors can make them a market moving force and attempt to identify proactive signals of such movements in the post activity of the forum, to be used as a part of a trading strategy. Data about the most mentioned stocks is collected, aggregated, combined with price data for the respective stock and analysed. Additionally, we utilise predictive modelling to be able to better classify trading signals. It is discovered that despite the considerable capital that retail investors can direct by coordinating their trading moves, additional factors, such as very high short interest, need to be present to achieve the volatility seen in the short squeeze wave. Furthermore, we find that autoregressive models are better suited to identifying signals correctly, with best results achieved by a Random Forest classifier. However, it became apparent that even the best performing model in our experimentation cannot make accurate predictions in extreme volatility, evidenced by the negative returns shown by conducted back-tests. Full article

A methodology to prove the influence of car configurator webpage data for automotive manufacturers is developed across this research. Firstly, the correlation between online data and sales is measured. Afterward, car variant sales are predicted using a set of forecasting techniques divided into univariate and multivariate ones. Finally, weekly color mix sales based on these techniques are built and compared. Results show that users visit car configurator webpages 1 to 6 months before the purchase date. Additionally, car variants predictions and weekly color mix sales derived from multivariate techniques, i.e., using car configurator data as external input, provide improvement up to 25 points in the assessment metric. Full article

Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, “Introduction to Ecological Forecasting,” at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students’ ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts. Full article

Climate change has caused uncertainty in the hydrological pattern including weather change, precipitation fluctuations, and extreme temperature, thus triggering unforeseen natural tragedies such as hurricanes, flash flooding, heatwave and more. Because of these unanticipated events occurring all around the globe, the study of the influence of climate change on the alteration of flooding patterns has gained a lot of attention. This research study intends to provide an insight into how the future projected streamflow will affect the flooding-inundation extent by comparing the change in floodplain using both historical and future simulated scenarios. For the future projected data, the climate model Atmosphere/Ocean General Circulation Model (AOGCM) developed by Coupled Model Intercomparison Project Phase 6 (CMIP6) is used, which illustrates that the flood is increasing in considering climate models. Furthermore, a comparison of the existing flood inundation map by the Federal Emergency Management Agency (FEMA) study with the map generated by future projected streamflow data presents the entire inundation area in flood maps, implying the expansion area compared to FEMA needs to be considered in making emergency response plans. The effect of flooding in the inundation area from historical to future flow values, presented mathematically by a calculation of inundation extent percentage, infers that the considered watershed of Rock River is a flood-prone area. The goal is to provide insights on the importance of using the forecasted data for flood analysis and to offer the necessary background needed to strategize an emergency response plan for flood management. Full article