Journal of Data Science

Forecasting business cycles and macroeconomic trends is inherently challenging due to their complex and non-linear relationships with volatile and noisy economic factors. However, the growing availability of large-scale economic data, coupled with advances in computational power, creates new opportunities to extract meaningful information and develop robust predictive models. Deep learning methods are well suited for handling noisy and complex data; nevertheless, their application to business cycle prediction remains at an early stage. To address this gap, this study develops an end-to-end computational framework that implements state-of-art deep learning architectures for identifying business cycle phases. The key contributions of this work include systematic input feature selection across a broad range of economic sectors, advanced preprocessing techniques for noisy data, the development of a customizable and reproducible computational framework, a data-driven approach to hyperparameter tuning, and the use of robust model selection strategies. Twenty two models based on two architectures—Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)—were implemented and demonstrated strong predictive performance, highlighting the effectiveness of the proposed approach for forecasting macroeconomic trends. Among these, the GRU model with 512 neurons achieved the best results, with an accuracy of 86.42%, precision of 92.03%, recall of 93.32%, and an F1-score of 92.34%. Overall, the findings provide valuable insights that can support informed decision-making by policymakers and other stakeholders.

Popular music genre preferences can be measured by consumer sales, listening habits, and critics’ opinions. We analyze trends in genre preferences from 1974 through 2018 presented in annual Billboard Hot 100 charts and annual Village Voice Pazz & Jop critics’ polls. We model yearly counts of appearances in these lists for eight music genres with two multinomial logit models, using various demographic, social, and industry variables as predictors. Since the counts are correlated over time, we use a partial likelihood approach to fit the models. Our models provide strong fits to the observed genre proportions and illuminate trends in the popularity of genres over the sampled years, such as the rise of country music and the decline of rock music in consumer preferences, and the rise of rap/hip-hop in popularity among both consumers and critics. We forecast the genre proportions (for consumers and critics) for 2019 using fitted multinomial probabilities constructed from forecasts of 2019 predictor values and compare our Hot 100 forecasts to observed 2019 Hot 100 proportions. We model over time the association between consumer and critics’ preferences using Cramér’s measure of association between nominal variables and forecast how this association might trend in the future.