Forecast Combination Research Articles

Modeling and Forecasting the Probability of Crypto-Exchange Closures: A Forecast Combination Approach

The popularity of cryptocurrency exchanges has surged in recent years, accompanied by the proliferation of new digital platforms and tokens. However, the issue of credit risk and the reliability of crypto exchanges remain critical, highlighting the need for indicators to assess the safety of investing through these platforms. This study examines a unique, hand-collected dataset of 228 cryptocurrency exchanges operating between April 2011 and May 2024. Using various machine learning algorithms, we identify the key factors contributing to exchange shutdowns, with trading volume, exchange lifespan, and cybersecurity scores emerging as the most significant predictors. Since individual machine learning models often capture distinct data characteristics and exhibit varying error patterns, we employ a forecast combination approach by aggregating multiple predictive distributions. Specifically, we evaluate several specifications of the generalized linear pool (GLP), beta-transformed linear pool (BLP), and beta-mixture combination (BMC). Our findings reveal that the beta-transformed linear pool and the beta-mixture combination achieve the best performances, improving forecast accuracy by approximately 4.1% based on a robust H-measure, which effectively addresses the challenges of misclassification in imbalanced datasets.

Peanut oil price change forecasts through the neural network

Purpose For a wide range of market actors, including policymakers, forecasting changes in commodity prices is crucial. As one of essential edible oil, peanut oil’s price swings are certainly important to predict. In this paper, the weekly wholesale price index for the period of January 1, 2010 to January 10, 2020 is used to address this specific forecasting challenge for the Chinese market. Design/methodology/approach The nonlinear auto-regressive neural network (NAR-NN) model is the forecasting method used. Forecasting performance based on various settings, such as training techniques, delay counts, hidden neuron counts and data segmentation ratios, are assessed to build the final specification. Findings With training, validation and testing root mean square errors of 5.89, 4.96 and 5.57, respectively, the final model produces reliable and accurate forecasts. Here, this paper demonstrates the applicability of the NAR-NN approach for commodity price predictions. Originality/value On the one hand, the findings may be used as independent technical price movement predictions. Conversely, they may be included in forecast combinations with forecasts derived from other models to form viewpoints of commodity price patterns for policy research.

Out-of-Sample Predictability of the Equity Risk Premium

A large set of macroeconomic variables have been suggested as equity risk premium predictors in the literature. Acknowledging the different predictability of the equity premium in expansions and recessions, this paper proposes an approach that combines equity premium forecasts from two-state regression models using an agreement technical indicator as the observable state variable. A comprehensive out-of-sample forecast evaluation exercise based on statistical and economic loss functions demonstrates the superiority of the proposed approach versus combined forecasts from linear models or Markov switching models and forecasts from machine learning methods such as random forests and gradient boosting. The parsimonious state-dependent aspect of risk premium forecasts delivers large improvements in forecast accuracy. The results are robust to sub-period analyses and different investors’ risk aversion levels.

Airbnb pricing in Sydney: predictive modelling and explainable machine learning

ABSTRACT We employ multiple predictive algorithms combined with explainable machine learning techniques to forecast and interpret Airbnb rental prices in Sydney, Australia. The best-performing model is selected using multiple metrics and model confidence sets from a variety of methods ranging from simple linear regression to more complex forecast combinations. In addition, we evaluate the importance of feature engineering by training the models on datasets constructed with and without feature engineering and assessing their respective accuracies. Ensemble methods, particularly stacking regressions, outperform other algorithms on both the training and test datasets, while linear models perform the worst. Factors such as property capacity, proximity to popular areas and luxury amenities increase price predictions according to Shapley values, whereas being near major highway entrances is linked to lower prices, likely due to noise and air pollution.

Common Mutual Information Selection Algorithm and Its Application on Combination Forecasting

Common Mutual Information Selection Algorithm and Its Application on Combination Forecasting

Formalizing a Postprocessing Procedure for Linear–Convex Combination Forecasts

ABSTRACTWe investigate mean squared forecast error (MSE) accuracy improvements for linear–convex combination forecasts, whose components are pretreated by a postprocessing procedure called “vector autoregressive forecast error modeling” (VAFEM). Assuming that the forecast error series of the individual forecasts are governed by a stable VAR process under classic conditions, we obtain the following results: (i) VAFEM treatment bias corrects all individual and linear–convex combination forecasts. (ii) Any VAFEM‐treated combination has a smaller theoretical MSE than its untreated analog, if the VAR parameters are known. (iii) In empirical applications, VAFEM gains depend on (1) in‐sample sizes, (2) out‐of‐sample forecast horizons, and (3) the biasedness of the untreated forecast combination. We demonstrate the VAFEM capacity in simulations and for realized‐volatility forecasting, using S&P 500 data.

On the Use of Hindcast Skill for Merging NMME Seasonal Forecasts across the Western United States

Abstract Multimodel ensemble forecasts have gained widespread use over the past decade. A yet unresolved issue is whether forecast skill benefits from the use of prior skill from each model in providing a weighted combination. Here, we use the available seasonal ensemble forecasts of six models from the North American Multi-Model Ensemble (NMME) to study various aspects of prior skill–based weighting schemes and explore ways to merge multimodel forecasts. First, we postprocess each NMME model through quantile mapping and a simple spread error adjustment. Then, using an equal weighted combination as the baseline forecast, we test merging the models together through skill-based weights by varying the prior skill metric and varying how the metrics are aggregated across the different subbasins and time of year. Results confirm prior work that the combined forecasts do outperform individual models. When evaluating prior skill, equal weighting generally performed as well as or slightly better than all weighting schemes tried. The skill of the weighting scheme was not found to be strongly dependent on prior metric but did improve when aggregating all forecasted months and subbasins together to provide one overall weight to each model. Also, we found that including an offset to the prior metric that nudged the weights closer to equal weighting improves skill especially at longer leads where individual model skill is low. Results also show that the weighting schemes performed better than regression-based techniques including multiple linear regression and random forest. Significance Statement Here, we test how effective the past performance of seasonal climate models can be used for generating weights to merge multiple models together using the North American Multi-Model Ensemble (NMME) for forecasting temperature and precipitation across the western United States. Our results showed there was little benefit in using prior performance compared to weighting all six NMME models equally. The performance of the weighted forecasts was not strongly dependent on the choice of performance metric or over how many months or basins performance was pooled. An important finding is that when only one or two models were used for merging, performance was reduced relative to equal weighting; however, if three or more models were used, performance was nearly the same.

PHOTOVOLTAIC GENERATION FORECASTING MODELS: CONCEPTUAL ENSEMBLE ARCHITECTURES

The decisions regarding power regulation, energy resource planning, and integrating “green” energy into the electrical grid hinge on precise probabilistic forecasts. One of the potential strategies to enhance forecast accuracy is the utilization of ensemble forecasting methods. They represent an approach where multiple models collaborate to achieve superior results compared to what a single model could produce independently. These methods can be categorized into two main categories: competitive and collaborative ensembles. Competitive ensembles harness the diversity of parameters and data to create a rich pool of base models. This approach may encompass statistical analysis, noise filtering, and anomaly elimination. On the other hand, collaborative ensembles rely on the interaction among models to achieve better outcomes. These methods encompass strategies such as weighted predictions, voting, aggregation, and a combination of model results. The research of ensemble forecasting methods in the context of photovoltaic generation is highly relevant, as solar energy represents a crucial source of renewable energy. Accurate predictions of solar energy production address the challenges related to the efficient utilization of photovoltaic panels and their integration into the overall energy system. This paper investigates conceptual ensemble architectures for photovoltaic energy forecasting. These architectures encompass various methods of aggregating base models within an ensemble, allowing for the consideration of different aspects and peculiarities of solar data, such as solar irradiation intensity, meteorological conditions, geographic factors, and more. These conceptual models are developed based on well-established statistical, machine learning, and artificial intelligence methods. Therefore, this paper provides an overview of ensemble forecasting methods for renewable energy, covering competitive and collaborative ensembles, as well as developing conceptual models for solar energy forecasting. This work aims to elevate the accuracy and efficiency of forecasts in the realm of renewable energy, representing a significant step in the advancement of sustainable and environmentally friendly energy production. Keywords: probabilistic solar forecasting, ensemble model, forecast combination, competitive ensembles, collaborative ensembles, conceptual models.

Combining Volatility Forecasts of Duration‐Dependent Markov‐Switching Models

ABSTRACTDuration‐dependent Markov‐switching (DDMS) models require a user‐specified duration hyperparameter, for which there is currently no established procedure for estimation or testing. As a result, an ad‐hoc duration choice must be heuristically justified. This paper proposes a methodology for handling duration selection in DDMS models, with a focus on volatility forecasting. The main novelty lies in generating forecasts through model combination techniques. The idea is that the combined forecasts will be more robust to misspecification in selecting the duration structure, thus yielding more accurate forecasts. Additionally, the paper contributes to the literature by evaluating the out‐of‐sample volatility forecasting performance of DDMS models compared to benchmark conditional volatility models. Empirical analysis involves returns from three distinct asset classes: a cryptocurrency, a stock market index, and a foreign currency exchange rate. Various volatility proxies and robust loss functions are incorporated into our analysis. The results indicate that combined forecasts outperform individual models and, in some cases, are more accurate than GARCH and MS‐GARCH models. Furthermore, models with fixed duration typically underperform relative to the simple GARCH model, often resulting in test rejections.

Tourism forecast combination using weighting schemes with flow information among component models

Forecast combination is an effective way to improve the accuracy of tourism demand forecasting. The continuous development of forecast combination methods with high accuracy is inevitable to help tourism practitioners formulate more appropriate management strategies. This study investigated how tourism forecasting accuracy can be improved by treating combination forecasting as a multiple attribute decision making (MADM) problem. The proposed hybrid methods first yield single-model forecasts from grey models without considering the sample size and limiting the available data to satisfy any statistical properties. Given the effectiveness of PROMETHEE in MADM, which applies flows to gauge the intensity of the preference for one alternative over another, the flows among component models in a combination are then used to assess relative weights next. Finally, the flow-based weighting schemes are incorporated into the linear and nonlinear combinations of individual forecasts. After assessing the accuracy of the proposed methods with the inbound tourism demand in Taiwan, the results indicated that the proposed methods involving the integration of the flow-based weighting scheme into the Choquet fuzzy integral performed better than other benchmark forecast combination methods with different model combinations.

Forecasting economic growth by combining local linear and standard approaches

Today, developing economies are of major importance for global macroeconomic development. However, the empirical analysis and especially the forecasting of macroeconomic time series remain difficult due to a lack of sufficient data, data frequency, high volatility, and non-linear developments. These difficulties require more sophisticated approaches to obtain reliable forecasts. Therefore, we propose an improved forecasting method especially for growth data based on a data-driven local linear trend estimation with an extended iterative plug-in algorithm for determining the bandwidth endogenously. This approach allows a smooth trend estimation that takes care of temporary changes in trend processes. Further, the naïve random walk model is extended for forecasting by including a local linear, time-varying drift. We apply this method to GDP development for six developing and two advanced economies and compare different forecast combinations. The combinations that include the local linear approach and the random walk with a local linear trend improve forecasting accuracy and reduce variance.

Forecasting exchange rate volatility: An amalgamation approach

The importance of exchange rate volatility forecasting has both practical and academic merit. Our aim is to provide a comprehensive analysis of the forecasting ability of financial and macroeconomics variables for future exchange rate volatility. We employ seven widely traded currencies against the US dollar and examine linear models and a variety of machine learning, dimensionality reduction and forecast combination approaches, along with creating a grand forecast (amalgamation approach) from these approaches. Our findings highlight the predictive power of the amalgamation approach, as well as the positive contribution of macroeconomic and financial variables in the forecasting experiment. Furthermore, we generate forecasts on the separate frequencies of volatility using wavelet analysis, in order to extract frequency-related information and examine timing effects in the performance of the methods.

Multi-criteria Forecast Combination Method with Nonlinear Programming for time series prediction models

Improving prediction computation for time series analysis is still a challenge. Finding a method that combines the benefits of different methodologies is still an open problem. Besides the very efficient prediction combination techniques proposed, there is still a lack of procedures that jointly consider error measure combinations and model constraints. In this work, we propose a new forecast combination procedure based on multi-criteria methods that allows the assignment of weights to different error measures in the objective function and the incorporation of constraints. A real case from the pharmaceutical industry for the sale of a probiotic product is presented to illustrate the performance of the proposal. This method is capable of considering different error measures and non distance based errors, is enriched by the consideration of constraints that consider desirable properties of the solution and is robust with respect to different time series characteristics such as trends, seasonality, etc. Results shows similar accuracy to the best known forecasting methods to date.

An energy management method for combined load forecasting of fuel cell ships

Abstract The article proposes an energy management method (EMM) for fuel cell ships, which consists of two parts: load forecasting (LF) and real-time optimized scheduling (RTOS). LF is composed of Long Short-Term Memory neural networks, which are capable of being trained to predict future loads based on historical ship load information. RTOS consists of model predictive control and can allocate energy in real time. Initially, shiploads and battery status are collected in real time, and then LF passes the predicted load sequence to RTOS. RTOS optimizes real-time energy allocation based on the predicted load and the current operating status of two sets of batteries, while meeting system constraints and maintaining the battery SOC at a healthy level, to minimize operational cost consumption. The simulations demonstrate that the EMM can consistently maintain SOC at a healthy level and reduce fuel cell ship operating costs to some extent, thereby improving economic efficiency.

Decision-focused linear pooling for probabilistic forecast combination

In real-world settings, decision-makers often have access to multiple forecasts for the same unknown quantity. Combining different forecasts has long been known to improve forecast quality, as measured by scoring rules in the case of probabilistic forecasting. However, improved forecast quality does not always translate into better decisions in a downstream problem that utilizes the resultant combined forecast as input. To this end, this work proposes a novel probabilistic forecast combination approach that accounts for the downstream stochastic optimization problem by which the decisions will be made. We propose a linear pool of probabilistic forecasts where the respective weights are learned by minimizing the expected decision cost of the induced combination, which we formulate as a nested optimization problem. Two methods are proposed for its solution: a gradient-based method that utilizes differential optimization layers, and a performance-based weighting method. The proposed decision-focused combination approach is validated in two integral problems associated with renewable energy integration in low-carbon power systems and compared against well-established combination methods. Namely, we examine an electricity market trading problem under stochastic solar production and a grid scheduling problem under stochastic wind production. The results illustrate that the proposed approach leads to lower expected downstream costs, while optimizing for forecast quality when estimating linear pool weights does not always translate into better decisions. Notably, optimizing for a combination of downstream cost and an accuracy-oriented scoring rule consistently leads to better decisions while also improving forecast quality.

Integrating seasonal forecast information with crop models to inform decision making in small-scale farming under climate variability

Integrating seasonal forecast information and crop models has the potential to inform farm management decisions under climate variability. The study assessed the feasibility of integrating seasonal forecast information into crop models for decision making in small-scale farming conditions in South Africa. Seasonal forecast outputs from the GCM, CFSv2, were coupled into the DSSAT v4.7 crop model to evaluate the impact of farm management decisions in Limpopo, South Africa. Historical weather and seasonal forecast data for the 2011–2017 and 2017/2018 seasons were utilised to set up and validate decision scenarios. The analysis of maize yield data under different combinations of management practices and seasonal forecasts yielded a range of decision scenarios. Overall, there were no notable differences in farm management decision scenarios among different farmer types. Integrating seasonal forecast information into crop models offers valuable insights in cases where decision capacity is low and climate sensitivity is high, as well as where decision capacity is high and climate sensitivity is weak. The decision support system proved more effective for cereal and vegetable crops than for legumes. In conclusion, integrating seasonal forecast information into crop models is a feasible approach for enhancing farm management decision making in South African small-scale farming systems.

Variable‐weight combined forecasting model with causal analysis and clustering for refined oil sales forecasting

AbstractForecasting refined oil sales is essential in energy supply chain management. However, accurate forecasting is limited by several factors, including multiple influences of external features, heterogeneity of different gasoline stations, and difficulty in balancing linear and nonlinear forecasting. To address these issues, we propose a novel variable‐weight combined forecasting model. In the first stage, the model incorporates causal analysis and clustering methods to provide a quantitative description of multiple effects of external features and highly correlated aggregation of homogeneous data. Subsequently, based on the patterns of external feature influences learned from historical data, variable‐weight combined forecasting is realized to balance linear and nonlinear forecasting dynamically. Experiments based on real sales data procured from several regions demonstrate that the proposed model outperforms other benchmark and widely used models in terms of forecasting accuracy and statistical significance. The ablation experimental results confirm the significance of causal analysis, clustering, and variable‐weight combined forecasting in improving the balance between linear and nonlinear forecasting. Moreover, our results indicate that improving the quality of clustering can yield greater benefits than improving the amount of training data. Finally, we also explore whether the forecasting superiority translates into better inventory control, and our results show that the proposed optimization model can effectively balance inventory cost and service level, while also better suppress the bullwhip effect.

Enhancing Agricultural Commodity Forecasting: A Median-Based Combination of Time Series Models

This study explores the efficacy of combining forecasts using the median operator to enhance forecasting performance. The traditional approach of assigning equal weights to individual models often struggles with extreme forecasts. A new method Simple Combination of Univariate Models (SCUM) is utilized, which uses the median operator to combine forecasts from four distinct time series models: Exponential Smoothing (ETS), Auto Regressive Integrated Moving Average (ARIMA), Dynamically Optimised Theta Model (DOTM), and Complex Exponential Smoothing (CES). This approach aims to mitigate the influence of extreme forecasts and improve overall accuracy. Our empirical analysis investigates the use of the SCUM approach for the agricultural commodity data. Yearly production of Rice is used, sourced from the Ministry of Agriculture & Farmers Welfare, Government of India. The forecasting performance of the SCUM approach is compared against individual models and a mean-based combined forecast using key performance metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Relative Efficiency. The results show that SCUM outperforms all other models, achieving the lowest RMSE (51.89) and a MAPE of 6.90, indicating superior in-sample forecasting accuracy. ARIMA was the least efficient with a 22.11% relative efficiency, while the Mean combination method (3.33%) was closest to SCUM in performance. The findings suggest that SCUM is not only a viable alternative to traditional methods but also offers significant advantages in improving forecasting accuracy. This research underscores the potential for median-based forecast combinations to achieve superior predictive accuracy and reliability, making it a valuable tool for scholars and practitioners in time series forecasting.

Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather Conditions

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.

Shapley‐value‐based forecast combination

AbstractThis paper puts forward a new and simple method to combine forecasts, which is particularly useful when the forecasts are strongly correlated. It is based on the Mincer Zarnowitz regression, and a subsequent determination using Shapley values of the weights of the forecasts in a new combination. For a stylized case, it is proved that such a Shapley‐value‐based combination improves upon an equal‐weight combination. Simulation experiments and a detailed illustration show the merits of the Shapley‐value‐based forecast combination.