Journal of Forecasting最新文献

By implementing a multi-objective optimization approach in forecasting, we introduce three optimization models grey wolf optimizer, genetic algorithm, and differential evolution algorithm combined with multilayer perceptron neural networks and support vector machines to predict electricity consumption in the UAE. The hybrid models' accuracy and efficiency were evaluated using various forecasting metrics. This study's contributions are threefold: it is the first to employ such a sophisticated hybrid approach, particularly using the recently introduced grey wolf optimizer, it compares optimization techniques with the established Pearson correlation-based method for dimensionality reduction and it represents one of the most extensive macroeconomic forecasts in the UAE using multi-objective heuristic hybrid optimization methods. Our findings indicate that the grey wolf optimizer significantly outperforms all other models, followed by the genetic algorithm.

Social media sentiment influences housing market trading and policy-making in China. To explore the multiscale relationship between social media sentiment and house price index (HPI) and improve prediction performance, a sentiment-based decomposition–ensemble approach is proposed for HPI forecasting. In this approach, five steps, that is, sentiment analysis for massive Weibo textual reviews about house prices, data decomposition for bivariate time series integrated by HPI and the sentiment index (SI), data smoothing for high-frequency components, component reconstruction for all individual modes, and all components prediction and ensemble, are involved. For verification, the National-level and two city-level house price indices are used as the sample data. The empirical results illustrate that the proposed approach can achieve better performance than all considered benchmark models at multi-step-ahead prediction horizons, indicating that it can be used as an effective tool for HPI forecasting.

Because the exchange rate is essentially a dynamic and nonlinear system, exchange rate forecasting has been one of the most challenging topics in the financial field. This paper proposes a novel idea of “decomposition-reconstruction-integration” to predict exchange rate. First, based on ICEEMDAN, the original sequences are decomposed into multifrequency IMFs. Second, we use t-test to determine the high-frequency IMFs, low-frequency IMFs, and trend sequence and reconstruct the high-frequency IMFs into a new component sequence. Third, we use CNN-LSTM model to predict these components separately and finally get the final prediction result by integration. This paper takes the USD/RMB exchange rate as research object, and the experimental results show that (1) the fluctuations of USD/RMB exchange rate are mainly affected by the trend sequence and low-frequency IMFs and are less affected by high-frequency IMFs. (2) The evaluation criterions RMSE, MAE, and MAPE of the ICEEMDAN-CNN-LSTM model are relatively small, with values of 0.0156, 0.0112, and 0.1679, respectively, indicating that the predictive performance of the model is optimal. (3) This paper has conducted various robust tests, all of which indicate that the proposed model has high prediction accuracy and stability. In summary, this paper has certain theoretical significance and application value.

Forecasting is a crucial part of supply chain management. Accurate forecasts have a strong influence on supply chain performance. Many forecasting methods have been developed and adapted in various domains and industries. However, none are perfect in all contexts due to the data's characteristics and the methods' strength. Hence, we propose a new ARIMAX-LSTM hybrid forecasting model that integrates ARIMAX and LSTM models to improve the ability to capture different combinations of linear and nonlinear patterns in time series. Our proposed model is validated in a case study of coffee demand in Vietnam. The case study results show that our proposed model outperforms the well-known single and current hybrid models regarding performance measures and degree of association. Moreover, to prove the model's robustness, we test and compare our proposed model to the previous study for Thailand's agricultural products (pineapple, corn, and cassava). Computational results demonstrate that our hybrid model is superior in the majority of experiments. It has a strong capability of predicting complex time series data. Furthermore, our proposed method increases forecasting accuracy and enhances supply chain performance (measured by the bullwhip effect; net-stock amplification, and transportation cost.

We derive the optimal forecasts for multivariate autoregressive time series processes subject to Markov switching in regime. Optimality means that the trace of the mean square forecast error matrix is minimized by using suitable weighting observations. Then we provide neat analytic expressions for the optimal weights in terms of the matrices involved in a state space representation of the considered process. Our matrix expressions in closed form improve computational performance since they are readily programmable. Numerical simulations and an empirical application illustrate the feasibility of the proposed approach. We provide evidence that the forecasts using optimal weights increase forecast precision and are more accurate than the traditional Markov switching alternatives.

The definition of interval-valued time series is now a valid tool that can be used to model uncertainty with known numerical bounds. However, how to provide accurate predictions of interval-valued time series remains an open problem. The goal of this paper is to develop a hybrid interval-valued time series prediction model that incorporates an intuitionistic fuzzy cognitive map and a fuzzy neural network. The causal relationship and adjacency matrix among nodes of the intuitionistic fuzzy cognitive map are defined and quantified using mutual subsethhood, in which the hesitation weight is added to the connection weight among concept nodes. The approach directly constructs concept nodes and a weight matrix for automatic recognition of intuitionistic fuzzy cognitive maps from original sequence data and combines the particle swarm optimization algorithm and back propagation algorithm to run with less manual intervention. The confidence intervals of forecasted interval values are also discussed. The developed prediction model is applied to forecast interval-valued financial time series (i.e., the Nasdaq-100 stock index), which is composed of daily minimum price and maximum price. The feasibility and validity of the proposed developed prediction model are shown through comparisons with some existing prediction models on interval-valued time series.

Cotton, cotton yarn, and other cotton products have frequent price volatility, increasing the difficulty for industry participants to develop rational business decision plans. To support cotton textile industry decision-makers, we apply data mining methods to extract the main influencing factors affecting cotton yarn futures prices from big data and build a probabilistic forecasting model for cotton yarn price volatility with uncertainty assessment. Based on Explainable Artificial Intelligence (XAI) and data-driven perspectives, we use the LassoNet algorithm to extract 18 features most relevant to the target variable from the massive data and visualize the importance values of the selected features to improve the reliability. Moreover, by combining conformal forecasting (CP) with quantile regression (QR), the uncertainty measure of the point estimation results of the long and short-term memory (LSTM) model is applied to improve the application value of the model. Finally, SHAP (SHapley Additive exPlanations) is introduced to analyze the SHAP values of the input features on the output results and to explore in depth the interaction and mechanism of action between the input features and the target variables to improve the explainability of the model. Our model provides a “big data-forecasting model-decision support” decision paradigm for real-world problems.