Forecasting, Volume 6, Issue 3 (September 2024) – 17 articles

Power consumption in the home has grown in recent years as a consequence of the use of varied residential applications. On the other hand, many families are beginning to use renewable energy, such as energy production, energy storage devices, and electric vehicles. As a result, estimating household power demand is necessary for energy consumption monitoring and planning. Power consumption forecasting is a challenging time series prediction topic. Furthermore, conventional forecasting approaches make it difficult to anticipate electric power consumption since it comprises irregular trend components, such as regular seasonal fluctuations. To address this issue, algorithms combining stationary wavelet transform (SWT) with deep learning models have been proposed. The denoised series is fitted with various benchmark models, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Bidirectional Gated Recurrent Units (Bi-GRUs), Bidirectional Long Short-Term Memory (Bi-LSTM), and Bidirectional Gated Recurrent Units Long Short-Term Memory (Bi-GRU LSTM) models. The performance of the SWT approach is evaluated using power consumption data at three different time intervals (1 min, 15 min, and 1 h). The performance of these models is evaluated using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The SWT/GRU model, utilizing the bior2.4 filter at level 1, has emerged as a highly reliable option for precise power consumption forecasting across various time intervals. It is observed that the bior2.4/GRU model has enhanced accuracy by over 60% compared to the deep learning model alone across all accuracy measures. The findings clearly highlight the success of the SWT denoising technique with the bior2.4 filter in improving the power consumption prediction accuracy. Full article

Electricity expense management presents significant challenges, as this resource is susceptible to various influencing factors. In universities, the demand for this resource is rapidly growing with institutional expansion and has a significant environmental impact. In this study, the machine learning models long short-term memory (LSTM), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost) were trained with historical consumption data from the Federal Institute of Paraná (IFPR) over the last seven years and climatic variables to forecast electricity consumption 12 months ahead. Datasets from two campuses were adopted. To improve model performance, feature selection was performed using Shapley additive explanations (SHAP), and hyperparameter optimization was carried out using genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate that the proposed cooperative ensemble learning approach named Weaker Separator Booster (WSB) exhibited the best performance for datasets. Specifically, it achieved an sMAPE of 13.90% and MAE of 1990.87 kWh for the IFPR–Palmas Campus and an sMAPE of 18.72% and MAE of 465.02 kWh for the Coronel Vivida Campus. The SHAP analysis revealed distinct feature importance patterns across the two IFPR campuses. A commonality that emerged was the strong influence of lagged time-series values and a minimal influence of climatic variables. Full article

With growing concerns over climate change, accurately predicting temperature trends is crucial for informed decision-making and policy development. In this study, we perform a comprehensive comparative analysis of four advanced time series forecasting models—Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), Multilayer Perceptron (MLP), and Gaussian Processes (GP)—to assess changes in minimum and maximum temperatures across four key regions in the United States. Our analysis includes hyperparameter optimization for each model to ensure peak performance. The results indicate that the MLP model outperforms the other models in terms of accuracy for temperature forecasting. Utilizing this best-performing model, we conduct temperature projections to evaluate the hypothesis that the rates of change in temperatures are greater than zero. Our findings confirm a positive rate of change in both maximum and minimum temperatures, suggesting a consistent upward trend over time. This research underscores the critical importance of refining time series forecasting models to address the challenges posed by climate change and supporting the development of effective strategies to mitigate the impacts of rising temperatures. The insights gained from this work emphasize the need for continuous advancement in predictive modeling techniques to better understand and respond to the dynamics of climate change. Full article

We analyze the predictability of daily data on the CBOE $VIX$ and $SKEW$ indices, used to capture the average level of risk-neutral risk and downside risk, respectively, as implied by S&P 500 index options. In particular, we use forecast models from the Heterogeneous Autoregressive ($HAR$) class to test whether and how lagged values of the $VIX$ and of the $SKEW$ may increase the forecasting power of $HAR$ for the $SKEW$ and the $VIX$. We find that a simple $HAR$ is very hard to beat in out-of-sample experiments aimed at forecasting the $VIX$. In the case of the $SKEW$, the benchmarks (the random walk and an $AR\left(1\right)$) are clearly outperformed by $HAR$ models at all the forecast horizons considered and there is evidence that special definitions of the $SKEW$ index based on put options data only yield superior forecasts at all horizons. Full article

Inundation maps that show water depths that occur in the event of a flood are essential for protection. Especially information on timings is crucial. Creating a dynamic inundation map with depth data in temporal resolution is a major challenge and is not possible with physical models, as these are too slow for real-time predictions. To provide a dynamic inundation map in real-time, we developed a data-driven multi-step inundation forecast system for fluvial flood events. The forecast system is based on a convolutional neural network (CNN), feature-informed dense layers, and a recursive connection from the predicted inundation at timestep t as a new input for timestep t + 1. The forecast system takes a hydrograph as input, cuts it at desired timesteps (t), and outputs the respective inundation for each timestep, concluding in a dynamic inundation map with a temporal resolution (t). The prediction shows a Critical Success Index (CSI) of over 90%, an average Root Mean Square Error (RMSE) of 0.07, 0.12, and 0.15 for the next 6 h, 12 h, and 24 h, respectively, and an individual RMSE value below 0.3 m, for all test datasets when compared with the results from a physically based model. Full article

In this study, we propose a model to forecast container throughput for the Singapore port, one of the busiest ports globally. Accurate forecasting of container throughput is critical for efficient port operations, strategic planning, and maintaining a competitive advantage. Using monthly container throughput data of the Singapore port from 2010 to 2021, we develop a Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model. For the exogenous variables included in the SARIMAX model, we consider the West Texas Intermediate (WTI) crude oil price and China’s export volume, alongside the impact of the COVID-19 pandemic measured through global confirmed cases. The predictive performance of the SARIMAX model was evaluated against a diverse set of benchmark methods, including the Holt–Winters method, linear regression, LASSO regression, Ridge regression, ECM (Error Correction Mechanism), Support Vector Regressor (SVR), Random Forest, XGBoost, LightGBM, Long Short-Term Memory (LSTM) networks, and Prophet. This comparative analysis was conducted by forecasting container throughput for the year 2022. Results indicated that the SARIMAX model, particularly when incorporating WTI prices and China’s export volume, outperformed other models in terms of forecasting accuracy, such as Mean Absolute Percentage Error (MAPE). Full article

Time series forecasting has witnessed a rapid proliferation of novel neural network approaches in recent times. However, performances in terms of benchmarking results are generally not consistent, and it is complicated to determine in which cases one approach fits better than another. Therefore, we propose adopting a data-centric perspective for benchmarking neural network architectures on time series forecasting by generating ad hoc synthetic datasets. In particular, we combine sinusoidal functions to synthesize univariate time series data for multi-input-multi-output prediction tasks. We compare the most popular architectures for time series, namely long short-term memory (LSTM) networks, convolutional neural networks (CNNs), and transformers, and directly connect their performance with different controlled data characteristics, such as the sequence length, noise and frequency, and delay length. Our findings suggest that transformers are the best architecture for dealing with different delay lengths. In contrast, for different noise and frequency levels and different sequence lengths, LSTM is the best-performing architecture by a significant amount. Based on our insights, we derive recommendations which allow machine learning (ML) practitioners to decide which architecture to apply, given the dataset’s characteristics. Full article

Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. The statistical properties of parameter and forecast estimates strongly depend on the structure of such matrices. The least squares (LS) method is the most flexible and can estimate systems of large dimensions; however, it is biased in the presence of multilateral (sparse) matrices. Instead, the unilateral specification of SAR models provides triangular weight matrices that allow consistent LS estimates and sequential prediction functions. These two properties are strictly related and depend on the linear and recursive nature of the system. In this paper, we show the better performance in out-of-sample forecasting of unilateral SAR (estimated with LS), compared to multilateral SAR (estimated with maximum likelihood, ML). This conclusion is supported by numerical simulations and applications to real geological data, both on regular lattices and irregularly distributed points. Full article

The main source of electricity worldwide stems from fossil fuels, contributing to air pollution, global warming, and associated adverse effects. This study explores wind energy as a potential alternative. Nevertheless, the variable nature of wind introduces uncertainty in its reliability. Thus, it is necessary to identify an appropriate machine learning model capable of reliably forecasting wind speed under various environmental conditions. This research compares the effectiveness of Dynamic Architecture for Artificial Neural Networks (DAN2), convolutional neural networks (CNN), random forest and XGBOOST in predicting wind speed across three locations in South Africa, characterised by different weather patterns. The forecasts from the four models were then combined using quantile regression averaging models, generalised additive quantile regression (GAQR) and quantile regression neural networks (QRNN). Empirical results show that CNN outperforms DAN2 in accurately forecasting wind speed under different weather conditions. This superiority is likely due to the inherent architectural attributes of CNNs, including feature extraction capabilities, spatial hierarchy learning, and resilience to spatial variability. The results from the combined forecasts were comparable with those from the QRNN, which was slightly better than those from the GAQR model. However, the combined forecasts were more accurate than the individual models. These results could be useful to decision-makers in the energy sector. Full article

In recent years, cryptocurrencies have received substantial attention from investors, researchers and the media due to their volatile behaviour and potential for high returns. This interest has led to an expanding body of research aimed at predicting cryptocurrency prices, which are notably influenced by a wide array of technical, sentimental, and legal factors. This paper reviews scholarly content from 2014 to 2024, employing a systematic approach to explore advanced quantitative methods for cryptocurrency price prediction. It encompasses a broad spectrum of predictive models, from early statistical analyses to sophisticated machine and deep learning algorithms. Notably, this review identifies and discusses the integration of emerging technologies such as Transformers and hybrid deep learning models, which offer new avenues for enhancing prediction accuracy and practical applicability in real-world scenarios. By thoroughly investigating various methodologies and parameters influencing cryptocurrency price predictions, including market sentiment, technical indicators, and blockchain features, this review highlights the field’s complexity and rapid evolution. The analysis identifies significant research gaps and under-explored areas, providing a foundational guideline for future studies. These guidelines aim to connect theoretical advancements with practical, profit-driven applications in cryptocurrency trading, ensuring that future research is both innovative and applicable. Full article

Deep neural networks (DNNs) are prominent in predictive analytics for accurately forecasting target variables. However, inherent uncertainties necessitate constructing prediction intervals for reliability. The existing literature often lacks practical methodologies for creating predictive intervals, especially for time series with trends and seasonal patterns. This paper explicitly details a practical approach integrating dual-output Monte Carlo Dropout (MCDO) with DNNs to approximate predictive means and variances within a Bayesian framework, enabling forecast interval construction. The dual-output architecture employs a custom loss function, combining mean squared error with Softplus-derived predictive variance, ensuring non-negative variance values. Hyperparameter optimization is performed through a grid search exploring activation functions, dropout rates, epochs, and batch sizes. Empirical distributions of predictive means and variances from the MCDO demonstrate the results of the dual-output MCDO DNNs. The proposed method achieves a significant improvement in forecast accuracy, with an RMSE reduction of about 10% compared to the seasonal autoregressive integrated moving average model. Additionally, the method provides more reliable forecast intervals, as evidenced by a higher coverage proportion and narrower interval widths. A case study on Thailand’s durian export data showcases the method’s utility and applicability to other datasets with trends and/or seasonal components. Full article

The electrification of the transport sector together with large renewable energy deployment requires powerful tools to efficiently use energy assets and infrastructure. In this framework, the forecast of electric vehicle demand and solar photovoltaic (PV) generation plays a fundamental role. This paper studies the impact of forecast accuracy on total electric cost of a simulated electric vehicles (EVs) charging station coupled with true solar PV and stationary battery energy storage. The optimal energy management system is based on the rolling horizon approach implemented in with a mixed integer linear program which takes as input the EV load forecast using long short-term memory (LSTM) neural network and persistence approaches and PV production forecast using a physical hybrid artificial neural network. The energy management system is firstly deployed and validated on an existing multi-good microgrid by achieving a discrepancy of state variables below 10% with respect to offline simulations. Then, eight weeks of simulations from each of the four seasons show that the accuracy of the forecast can increase operational costs by 10% equally distributed between the PV and EV forecasts. Finally, the accuracy of the combined PV and EV forecast matters more than single accuracies: LSTM outperforms persistence to predict the EV load (−30% root mean squared error), though when combined with PV forecast it has higher error (+15%) with corresponding higher operational costs (up to 5%). Full article

This study showcased the Markov switching autoregressive model with time-varying parameters (MSAR-TVP) for modeling nonlinear time series with structural changes. This model enhances the MSAR framework by allowing dynamic parameter adjustments over time. Parameter estimation uses maximum likelihood estimation (MLE) enhanced by the Kim filter, which integrates the Kalman filter, Hamilton filter, and Kim collapsing, further refined by the Nelder–Mead optimization technique. The model was evaluated using U.S. real gross national product (GNP) data in both in-sample and out-of-sample contexts, as well as an extended dataset to demonstrate its forecasting effectiveness. The results show that the MSAR-TVP model improves forecasting accuracy, outperforming the traditional MSAR model for real GNP. It consistently excels in forecasting error metrics, achieving lower mean absolute percentage error (MAPE) and mean absolute error (MAE) values, indicating superior predictive precision. The model demonstrated robustness and accuracy in predicting future economic trends, confirming its utility in various forecasting applications. These findings have significant implications for sustainable economic growth, highlighting the importance of advanced forecasting models for informed economic policy and strategic planning. Full article

The COVID-19 pandemic has affected all Sustainable Development Goals (SDGs), leading to setbacks in various Latin American countries. In Brazil, progress in technological development and the adoption of sustainable practices by organizations has been significantly hindered. Yet, there remains a limited understanding of the long-term impacts on the country’s development, and a structured national plan for recovery and resuming progress toward the SDGs is lacking. This paper aims to investigate the repercussions of COVID-19 on SDGs 9 (industry, innovation, and infrastructure) and 12 (sustainable consumption and production) in the context of a latecomer country such as Brazil. This study adopted the Delphi-based scenario and Fuzzy Delphi approach and involved the participation of 15 sustainability experts with extensive experience in the Brazilian industrial sector. The findings elucidate the long-term impacts of the pandemic on these SDGs, focusing on Brazil’s socioeconomic landscape and developmental challenges. The pandemic worsened pre-existing issues, hindering infrastructure modernization, technological investment, and sustainable practices. Insufficient research funding, industry modernization, and small business integration further impede progress. Additionally, the paper identifies implications for research, companies, and public policies, aiming to provide actionable insights for fostering sustainable development in the post-pandemic era. Full article

Previous research provides conflicting results regarding how R&D expenditures impact market value. Given that financial analysts are the primary intermediaries between companies and investors, our study focused on the impact of R&D-related uncertainty, growth, and information asymmetry associated on analysts’ earnings forecasts. Based on 19,834 firm-year observations in the European market between 2005 and 2020, our results show that R&D activities lead to higher absolute forecast error and negative forecast error, indicating higher forecast inaccuracy with an optimistic bias. Additionally, these investments contribute to higher forecast dispersion, indicating disagreement among financial analysts. The comparison between 17 industries revealed that these effects are more pronounced in R&D-intensive industries than in non-R&D industries, uncovering the varied relationship between R&D investments and analyst forecasts across sectors. Full article

In a dynamic business environment, the accuracy of sales forecasts plays a pivotal role in strategic decision making and resource allocation. This article offers a systematic review of the existing literature on techniques and methodologies used in forecasting, especially in sales forecasting across various domains, aiming to provide a nuanced understanding of the field. Our study examines the literature from 2013 to 2023, identifying key techniques and their evolution over time. The methodology involves a detailed analysis of 516 articles, categorized into classical qualitative approaches, traditional statistical methods, machine learning models, deep learning techniques, and hybrid approaches. The results highlight a significant shift towards advanced methods, with machine learning and deep learning techniques experiencing an explosive increase in adoption. The popularity of these models has surged, as evidenced by a rise from 10 articles in 2013 to over 110 by 2023. This growth underscores their growing prominence and effectiveness in handling complex time series data. Additionally, we explore the challenges and limitations that influence forecasting accuracy, focusing on complex market structures and the benefits of extensive data availability. Full article

This research aims to study and develop a model to demonstrate the causal relationships of factors used to forecast CO₂ emissions from energy consumption in the industrial building sector and to make predictions for the next 10 years (2024–2033). This aligns with Thailand’s goals for sustainability development, as outlined in the green economy objectives. The research employs a quantitative research approach, utilizing Linear Structural Relationships based on a Latent Growth Model (LISREL-LGM model) which is a valuable tool for efficient country management towards predefined green economy objectives by 2033. The research findings reveal continuous significant growth in the past economic sector (1990–2023), leading to subsequent growth in the social sector. Simultaneously, this growth has had a continuous detrimental impact on the environment, primarily attributed to the economic growth in the industrial building sector. Consequently, the research indicates that maintaining current policies would result in CO₂ emissions from energy consumption in the industrial building sector exceeding the carrying capacity. Specifically, the growth rate (2033/2024) would increase by 28.59%, resulting in a surpassing emission of 70.73 Mt CO₂ Eq. (2024–2033), exceeding the designated carrying capacity of 60.5 Mt CO₂ Eq. (2024–2033). Therefore, the research proposes strategies for country management to achieve sustainability, suggesting the implementation of new scenario policies in the industrial building sector. This course of action would lead to a reduction in CO₂ emissions (2024–2033) from energy consumption in the industrial building sector to 58.27 Mt CO₂ Eq., demonstrating a decreasing growth rate below the carrying capacity. This underscores the efficacy and appropriateness of the LISREL-LGM model employed in this research for guiding decision making towards green economy objectives in the future. Full article