Journal of Forecasting最新文献

Matrix- and tensor-valued time series models have emerged as effective tools to address the challenges posed by high-dimensional time series data. These models utilize the multi-classification structures inherent in data variables to decompose large interaction networks into smaller, more manageable sub-networks. To further reduce dimensionality, recent research has explored regularized matrix-valued time series models. This study builds upon this line of work by proposing the RR-S-MAR model—a matrix autoregressive (MAR) model that incorporates a reduced-rank structure on one side and a sparse structure on the other. We address key challenges related to the estimation, inference, and selection of the proposed model. For regularized estimation, we develop an alternating least-squares algorithm, while statistical inference is conducted using a bootstrapping method. To optimize the selection of rank and sparsity level, we introduce an extended Bayesian information criterion (EBIC). Simulation studies demonstrate the convergence of the estimation algorithm and validate the effectiveness of the proposed model selection criterion. Finally, we apply the RR-S-MAR model to economic data, showcasing its practical utility and providing insights through real-world analysis and interpretation.

A dynamic evolving fuzzy system (eFSi) method for interval-valued time series (ITS) data modeling and forecasting is suggested in this paper. The eFSi method simultaneously adapts the structure and the parameters of the models that it develops whenever it processes a new input data. Essentially, an eFSi model is a collection of interval-valued functional fuzzy rules. The participatory learning algorithm is used to identify the antecedents of the rules and the structure of the model. The parameters of the rule consequent are estimated using the recursive weighted least squares algorithm modified to handle the center and range representation of interval-valued data. Computational experiments are conducted to forecast financial high and low prices of different markets such as stocks, exchange rate, energy commodity, and cryptocurrency. The accuracy of one-step-ahead forecasts produced by the eFSi models are compared with classic, machine learning, and interval-valued methods. Economic evaluation of the models is done using the forecasts to predict range-based volatility. For both, high and low prices of S&P 500, EUR/USD, WTI crude oil, and Bitcoin, out-of-sample evaluations indicate that the interval-valued approaches offer more accurate forecasts because they process the data and produces forecasts that account for their intrinsic interval nature. In range-based volatility estimation, the eFSi generally achieves the highest accuracy. The interval-valued eFSi model emerges as a powerful prospective tool for ITS prediction.

The accurate prediction of the Coal Index is vital due to its substantial impact on economic and environmental policy. This study represents a significant advancement in the field of coal index forecasting by introducing a hybrid deep learning model that effectively tackles the challenge of nonlinear time series data. This innovation overcomes the limitations of traditional statistical and basic machine learning approaches. The core of this model is a unique combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), sentiment analysis from a multicloud platform, and a gated recurrent unit (GRU) with an attention mechanism. This research marks the inaugural application of sentiment analysis in the predictive domain of the coal industry, enhancing predictive accuracy. In this research, the CEEMDAN method is applied to decompose China's coal index data from March 2015 to November 2023, which, in conjunction with the sentiment analysis results, are processed using an attention-GRU layer to enhance the accuracy and depth of forecasting. Experimental results demonstrate that the proposed model achieves superior performance over several benchmarks in accuracy and error reduction. These results underscore the potential of advanced, integrated analytical techniques in enhancing economic forecasting models.

Reliable forecasts of infectious disease trajectories are indispensable for timely public health action and allocation of medical resources. However, most time-series forecasting frameworks still rely solely on historical case counts and thus struggle to capture sudden shifts in population behavior. Therefore, to quantify the value of external behavioral signals during the COVID-19 pandemic, this research assembled a 124-week (from May 31, 2020, to October 9, 2022) panel that fuses Google Community-Mobility indices with standard surveillance indicators such as new cases, deaths, tests, and vaccinations plus information about population density and the Oxford policy-stringency score for 20 countries spanning six continents. We proceed to assess two forecasting methodological families for predicting new cases using an 8-week hold-out window. The target-variable-only family comprised models using a 4-week rolling average, autoregressive integrated moving average (ARIMA), Prophet, and long short-term memory (LSTM) approaches. In contrast, the data-integration family employs distinct light gradient boosting machine (LightGBM) variants: LightGBM-Direct, which learns a single multi-output mapping for all periods in the horizon, and LightGBM-Recursive, which updates a one-step model and rolls its predictions forward. Performance is evaluated using root mean square error (RMSE) and two optimized weight indices (OWIs), which benchmark improvements over the rolling-average baseline and ARIMA, respectively. The results demonstrate that a mobility-enhanced LightGBM achieves the lowest RMSE in every country, reducing the overall median error by 83% compared with the baseline and by 87% against ARIMA. LightGBM-Direct excels in twelve nations, characterized by smoother trends, whereas LightGBM-Recursive dominates in the remaining eight, which exhibit rapid fluctuations in incidence. Notably, SHapley Additive exPlanations (TreeSHAP) identifies workplace and transit-station mobility, testing intensity, vaccinations, and policy stringency as the most influential predictors, denoting the importance of external behavioral signals in improving pandemic forecast accuracy.

Influenced by various complex factors, the price series of agricultural futures exhibit nonstationarity. Existing research often presumes that the relationship between inputs and outputs remains stable throughout the training process. This assumption makes it challenging to dynamically adjust the weights of various models based on data characteristics. Furthermore, existing studies focus only on modeling variable dependencies, overlooking the impact of variable independence on model robustness. Therefore, this paper proposes a two-stream ensemble forecasting model that integrates a dynamic sentiment index. Initially, ChineseBERT and textCNN are employed to classify the sentiment of news texts, calculating the sentiment scores. Subsequently, weight factors are designed based on daily price fluctuations to adjust these sentiment scores, ensuring they accurately reflect the impact of news sentiment on market prices. In the model construction phase, multivariate time series data are input into two distinct models: one model is dedicated to capturing temporal dependencies, while the other focuses on capturing intervariable dependencies, thereby providing diverse yet complementary predictive insights. An online convex optimization approach is then utilized to learn the optimal combination weights. During the testing phase, reinforcement learning is applied to dynamically adjust the prediction weights of these two models. The effectiveness of the proposed methods is validated using soybean and corn futures prices. Experimental results demonstrate that the proposed two-stage sentiment index (TPSI) exhibits strong predictive capability for agricultural futures prices, achieving high accuracy in short-term and medium-term price forecasts.

This study examines the effectiveness of different predictors to forecast volatility of E7 emerging markets. First, we employ the economic uncertainty factors information to find out the economic impact on stock market volatility. Second, we used the geopolitical uncertainty factor information to explore the influence of geopolitical events on stock market volatility. Third, we investigate the climate risk factors to forecast the stock market volatility. Fourth, we examine the energy market–related uncertainty factors' impact on equity market volatility (EMV). The out-of-sample results show that among all predictors, economic policy uncertainty (EPU), EMV, geopolitical risk (GPR), and the environmental social and governance (ESG) index have the better forecasting ability to predict stock market volatility. Additionally, we find evidence during different business conditions, recession and expansion, and the most recent COVID-19 pandemic. The results are identical during recession periods and COVID-19 pandemic. Notably, these indices proved their superior predictive performance for volatility estimation. Finally, to ensure the robustness of our findings, we use different forecasting window method. Considering a range of uncertainty factors allows for a more comprehensive understanding of market fluctuations. These results suggest that policymakers and investors should consider the volatility dynamics of uncertainty factors in financial decision-making and policy realms.

With Covid-19 and Russian invasion of Ukraine triggering vast uncertainty, non-traditional (potentially big) data sources could provide timely information on the state of the economy. We investigate the usefulness of big data during these events in a bottom-up nowcasting exercise for Polish food inflation. We show that information embedded in roughly 250 million web-scraped prices makes it possible to closely track official inflation and provides the most accurate nowcasts in times of elevated uncertainty and volatility, while competing model-based frameworks skew nowcasts towards historical patterns, which leads to much lower accuracy. In turn, during normal times, time series models with web-scraped prices provide slightly more accurate nowcasts but the difference in predictive quality is negligible in comparison to nowcasts based purely on online prices. We also extensively experiment on how to optimally aggregate daily online prices. For practitioners we formulate four guidelines. First, let big data speak, especially during uncertain times. Second, model-based frameworks with online prices are not necessary to obtain precise nowcasts of price developments, even on long samples. Third, simplest aggregation methods of individual product prices lead to the most accurate nowcasts. Fourth, for nowcasting quality the gain from observing prices daily is marginal compared to weekly frequency of data collection.

The rapid expansion of Internet infrastructure and artificial intelligence (AI) has significantly advanced intelligent transportation systems (ITS), which are considered as essential for automating traffic monitoring and management in smart cities. Among ITS applications, traffic flow and density prediction are considered as important problem for optimizing transportation planning and reducing congestion. In recent years, deep learning models, particularly recurrent neural networks (RNNs) and graph neural networks (GNNs), have been widely utilized for traffic forecasting. These models can support to effectively capture temporal and spatial dependencies in traffic data, as a result enabling more accurate forecasting. Despite advancements, recently proposed RNN-GNN-based forecasting models still face challenges related to the capability of preserving rich structural and topological features from traffic networks. The complex spatial dependencies inherent in road connections and vehicle movement patterns are often underrepresented; therefore, limiting the forecasting accuracy. To address these limitations, in this paper, we propose SGL4TF, a structure-enhanced graph learning model that integrates graph convolutional networks (GCN) with a sequence-to-sequence (seq2seq) framework. This architecture enhances the ability to jointly model spatial relationships and long-term temporal dependencies, hence can lead to more precise traffic predictions. Our approach introduces a deeper graph-structural learning mechanism using nonlinear transformations within GNN layers, which can effectively assist to improve structural feature extraction while mitigating over-smoothing issues. The seq2seq component further refines temporal correlations, enabling long-term traffic state predictions. Extensive experiments on real-world datasets demonstrate our proposed SGL4TF model's superior performance over state-of-the-art traffic forecasting techniques.