Combined Forecasting Method Research Articles

Hybrid wind speed optimization forecasting system based on linear and nonlinear deep neural network structure and data preprocessing fusion

Wind speed time series forecasting has been widely used in wind power generation. However, the nonlinear and non-stationary characteristics of wind speed make accurate wind speed forecasting a difficult task. In recent years, the rapid development of artificial intelligence and machine learning technology provides a new solution to the problem of wind speed forecasting. Combining the advance of artificial intelligence and data analysis strategy, this paper proposes a wind speed forecasting system based on multi-model fusion and integrated learning. Considering the differences in data observation and training principles of various algorithms and exploiting the advantages of each model, a wind speed forecasting system with multiple machine learning algorithms embedded in integrated learning is constructed, which includes three modules: data preprocessing, optimization and forecasting. The data preprocessing module can conduct quantitative analysis through input data decomposition and feature extraction, and the combination of multi-objective intelligent optimization algorithm and combined forecasting method can effectively forecast the wind speed time series. The validity of the algorithm is verified using the data of Shandong wind farm in China. The forecasting results show that compared with the traditional single model forecasting, the proposed integrated wind speed forecasting system based on the fusion of multiple models has higher forecasting accuracy.

A convolutional neural network–back propagation based three-layer combined forecasting method for spare part demand

Accurate spare part demand forecasting for the key components of ships is one of the key factors to ensure normal ship operation. Large errors in demand forecasting not only bring challenges to the normal operation of ships but also cause an inventory backlog of key components and thus increase the operation and maintenance costs. A three-level spare parts combination prediction method based on historical data has been proposed, aiming to solve the problem of insufficient data in existing prediction methods. First, three types of individual direct forecasting models are used for predictions. Secondly, we used convolutional neural networks to perform convolutional operations on the prediction results, and then constructed a three-layer combined prediction model using backpropagation neural networks (BP). Experimental results have shown that the predictive performance of this model is significantly better than that of single-layer models. This study used spare parts data from shipping companies to predict the three-layer combination model, and the results fully demonstrated its significant advantages over single-layer models.

A Wind Power Combination Forecasting Method Based on GASF Image Representation and UniFormer

In the field of wind power prediction, traditional methods typically rely on one-dimensional time-series data for feature extraction and prediction. In this study, we propose an innovative short-term wind power forecasting approach using a “visual” 2D image prediction method that effectively utilizes spatial pattern information in time-series data by combining wind power series and related environmental features into a 2D GASF image. Firstly, the wind power data are decomposed using the ICEEMDAN algorithm optimized by the BWO (Beluga Whale Optimization) algorithm, extracting the submodal IMF (Intrinsic Mode Function) components with different frequencies. Then, modal reconstruction is performed on the basis of the permutation entropy value of the IMF components, selecting meteorological features highly correlated with reconstructed components through Spearman correlation analysis for data splicing and superposition before converting them into GASF images. Finally, the GASF images are input into the UniFormer model for wind power sequence prediction. By leveraging wind power data predictions from a coastal wind farm in East China and Sotavento in Spain, this study demonstrates the significant benefits and potential applications of this methodology for precise wind power forecasting. This research combines the advantages of image feature extraction and time-series prediction to offer novel perspectives and tools for predicting renewable energy sources such as wind power.

A paradigm shift in solar energy forecasting: A novel two-phase model for monthly residential consumption

Accurately predicting residential solar energy consumption is crucial for efficient electricity production, supply, and power dispatch. However, conventional forecasting methods often struggle to handle complex energy consumption data. In response to this challenge, this study develops a pioneering two-stage error-corrected combined forecasting model that integrates traditional linear methods, seasonal processing techniques, deep learning models, and intelligent optimization algorithms to outperform other combined forecasting methods in terms of performance. This research analyzes the combined weight values, shedding light on why the proposed model consistently outperforms its counterparts. To confirm its superiority, the proposed model and five benchmark models are rigorously tested in this paper using four evaluation metrics and a hypothesis testing method. The empirical results show that the proposed combined model performs well in terms of accuracy and stability. Notably, the average absolute percentage error of its 24-step ahead prediction is 2.9053 %, which outperforms all comparative models, both single and combined model. These results fully illustrate the advantages of the combined model and reaffirm the excellence of its prediction performance in predicting energy consumption.

Oil price volatility prediction using out-of-sample analysis – Prediction efficiency of individual models, combination methods, and machine learning based shrinkage methods

In this study, we compare the efficacy of forecast combination methods against machine learning-based shrinkage techniques in predicting oil price volatility. Our analysis is based on heterogeneous autoregressive (HAR) model framework. We employ eight individual HAR models and their variations, alongside five distinct combination methods for aggregating forecasts derived from HAR models and their variants. Additionally, we incorporate two widely recognized machine learning-based shrinkage methods, namely the elastic net and the lasso. Machine learning (ML) techniques, including elastic net and lasso, exhibit promise in estimating individual extended HAR models and combination sampled approaches. Meanwhile, model confidence set (MCS) estimation techniques demonstrate notably superior out-of-sample forecasting performance for the chosen sample. Our empirical findings reveal that both the elastic net and the lasso exhibit superior out-of-sample prediction accuracy in comparison to the individual HAR models and their variants, as well as the five combination techniques. Furthermore, we provide statistical evidence demonstrating the notably higher directional accuracy achieved by the elastic net and lasso methodologies. Importantly, our results remain statistically consistent across a range of robustness analyses. These findings hold significance for investors and policymakers, as they suggest potential economic benefits derived from allocating portfolios in alignment with oil price volatility estimates.

Regional short-term load forecasting method based on power load characteristics of different industries

With the gradual increase in the proportion of end-user electricity consumption, the electrical load has become a crucial issue in carbon emission studies. Precise load forecasting will benefit the treatment, calculations, and markets of carbon emission. To explore the characteristics of industrial power load in depth and improve the accuracy of regional short-term load forecasting, this paper proposes a regional short-term load forecasting method based on the power load characteristics of different industries. First, a power load characteristic index system is established to obtain a fine industry classification. Considering the influence of external factors on the power load, the industries are then further classified depending on their sensitivity to external factors using a combination of grey correlation analyses. For the non-external factor-sensitive industries, an integrated model based on the Shapley value method is adopted; for the external factor-sensitive industries, a factor analysis-bidirectional gated recurrent unit-attention mechanism model is proposed. The final combination of the two is the output of the total regional load forecasting results. The combined forecasting method is tested using power load data from a city in Liaoning, China. The results show that the proposed method has apparent advantages over the traditional single forecasting model in terms of forecasting accuracy.

Short Term Power Load Combination Forecasting Method Based on Feature Extraction

Short Term Power Load Combination Forecasting Method Based on Feature Extraction

Forecasting stock volatility using pseudo-out-of-sample information

This paper proposes a novel forecast combination method and investigates the impact of technical indicators on volatility prediction in the Chinese stock market. Firstly, our analysis reveals that technical indicators based on good and bad volatility have a stronger explanatory power on stock volatility, exhibiting an evident asymmetric effect. In addition, we introduce two new categories of technical indicators based on price skewness risk and kurtosis risk, which exhibit more robust and significant predictive power on volatility than existing indicators. Finally, we propose a new forecast combination method that employs adjusted out-of-sample R2 as a weight, which outperforms a series of existing forecasting models. These findings are robustly confirmed in multiple robust tests, demonstrating the efficacy of our proposed approach.

International commodity market and stock volatility predictability: Evidence from G7 countries

The existing literature has extensively documented the close connection between commodity and stock markets. This empirical study investigates the predictive role of seven metal commodities (gold, silver, aluminum, lead, copper, zinc, and nickel) in G7 stock volatility. The results of the individual factor analysis indicate that gold and silver outperform their peers in both statistical and economic evaluation methods. To extract comprehensive predictive content, we further investigate the forecasting performance using combination forecast, dimension reduction, and shrinkage methods. Moreover, our empirical results indicate that commodity volatility information can transmit to international equity markets through the ratio of gold to platinum prices (GP) and the ratio of silver to platinum prices (SP).

A modified weighting system for combined forecasting methods based on the correlation coefficients of the individual forecasting models

Herein, a modified weighting for combined forecasting methods is established. These weights are used to adjust the correlation coefficient between the actual and predicted values from five individual forecasting models based on their correlation coefficient values and ranking. Time-series datasets with three patterns (stationary, trend, or both trend and seasonal) were analyzed by using the five individual forecasting models and three combined forecasting methods: simple-average, Bates-Granger, and the proposed approach. The MAPE and RMSE results indicate that the proposed method outperformed the others, especially when the time-series pattern was stationary and improved the forecasting accuracy of the worst and best individual forecasting models by 35–37% and 7–10%, respectively. Moreover, the proposed method showed improvements in MAPE and RMSE of around 18–20% and 9–11% compared to the simple-average and Bates-Granger methods, respectively. In addition, the combined forecasting methods outperformed the individual forecasting models when analyzing non-stationary data. Remarkably, the performances of the proposed and Bates-Granger methods were almost the same, with improvements in MAPE and RMSE in the range of 1–2% on average. Therefore, the proposed method for creating weights based on the correlation coefficients of the individual forecasting models greatly improves combined forecasting methods.

Comparison of combination methods to create calibrated ensemble forecasts for seasonal influenza in the U.S.

The characteristics of influenza seasons vary substantially from year to year, posing challenges for public health preparation and response. Influenza forecasting is used to inform seasonal outbreak response, which can in turn potentially reduce the impact of an epidemic. The United States Centers for Disease Control and Prevention, in collaboration with external researchers, has run an annual prospective influenza forecasting exercise, known as the FluSight challenge. Uniting theoretical results from the forecasting literature with domain-specific forecasts from influenza outbreaks, we applied parametric forecast combination methods that simultaneously optimize model weights and calibrate the ensemble via a beta transformation and made adjustments to the methods to reduce their complexity. We used the beta-transformed linear pool, the finite beta mixture model, and their equal weight adaptations to produce ensemble forecasts retrospectively for the 2016/2017, 2017/2018, and 2018/2019 influenza seasons in the U.S. We compared their performance to methods that were used in the FluSight challenge to produce the FluSight Network ensemble, namely the equally weighted linear pool and the linear pool. Ensemble forecasts produced from methods with a beta transformation were shown to outperform those from the equally weighted linear pool and the linear pool for all week-ahead targets across in the test seasons based on average log scores. We observed improvements in overall accuracy despite the beta-transformed linear pool or beta mixture methods' modest under-prediction across all targets and seasons. Combination techniques that explicitly adjust for known calibration issues in linear pooling should be considered to improve probabilistic scores in outbreak settings.

Prediction of Corona Virus Deaths Based on Big Data Analysis - Focusing on the Change Point Detection and the Forecast Combination method -

Prediction of Corona Virus Deaths Based on Big Data Analysis - Focusing on the Change Point Detection and the Forecast Combination method -

Forecasting stock market volatility with various geopolitical risks categories: New evidence from machine learning models

This paper investigates how geopolitical risks influence the prediction performance on the US stock market volatility with machine learning models. Further, it compares the predictive performance of individual and combination forecast methods. With SHAP algorithm, it could identify which factor has a great impact and fully extract the information of geopolitical risks in predicting. Empirical results show that military build-ups and escalation of war have great importance on predicting realized volatility among various geopolitical risks. The research further emphasizes the superior performance of machine learning and forecast combination methods, especially SVR method and trimmed mean combination. In addition, by allocating portfolio according to the machine learning-based volatility forecasts, particularly elastic net and random forest, a mean-variance investor can achieve sizeable financial benefits. Our paper provides substantial implications for political risk management and volatility forecasting.

Carbon price prediction based on LsOALEO feature selection and time-delay least angle regression

Accurate carbon price prediction can help governments to improve the environment and reduce carbon emissions at a lower cost. To improve the prediction accuracy of carbon price, many scholars have attempted to use multiple factors to predict the carbon price. This method improves carbon price prediction, but also brings problems such as increased complexity and decreased interpretability of prediction models. When analyzing the influencing factors of carbon price, scholars tend to overlook the coupling relationship between feature selection and time delay, resulting in redundant or missing influencing factors. To solve the above problems, we innovatively propose a carbon price prediction model based on LsOALEO feature selection and time-delay least angle regression (LsOALEO-FSTDLARS). The model is mainly composed of two parts: (1) Based on LARS, the prediction model is constructed by integrating feature selection and time delay, and the feasibility of the model is proved theoretically. (2) On the basis of lioness optimization algorithm, the local escape operator is used to improve the global search ability of lioness optimization algorithm, and the algorithm is used for feature selection and time delay optimization. Finally, we conduct experiments on the data sets of the carbon markets in Guangdong, Shanghai, Hubei, Tianjin and China, and compares the proposed method with various other prediction methods. The results of empirical analysis show that compared with the best performing single and combined forecasting methods, LsOALEO-FSTDLARS has an average reduction of 31.05% and 1.30% in RMSE, 46.62% and 5.37% in MAPE, and an average increase of 31.39% and 0.94% in R2 respectively. All of the above indicate that the LsOALEO-FSTDLARS model has higher prediction accuracy, stronger generalization ability and more stable effect, and can be used as an effective tool for carbon price prediction.

Overview of Logistics Demand Forecasting Methods

Accurate forecasting of logistics demand is of great theoretical significance and practical application value for the formulation of national policies and the satisfaction of actual demand in the logistics industry. From the modeling form, the existing logistics demand forecasting methods are divided into four categories: single traditional forecasting method, single intelligent forecasting method, combined forecasting method and mixed forecasting method. Among them, single traditional forecasting methods mainly include simple time series method, regression analysis, mathematical and statistical methods, etc.; single intelligent forecasting methods mainly involve gray forecasting method, neural network, support vector machine and their improved forms; combined forecasting methods are mainly summarized into three combined forms: linear combination of single forecasting results, nonlinear combination of single forecasting results, modified single forecasting results; mixed forecasting methods are mainly summarized into three hybrid forms: hybrid intelligent optimization algorithms with single prediction methods, hybrid data dimensionality reduction techniques with intelligent prediction methods, and hybrid data mining techniques with intelligent prediction methods. The four major types of forecasting methods are reviewed, and each forecasting model in the four major types of methods is evaluated in terms of modeling principles, advantages and disadvantages, and applicability, in order to find forecasting methods suitable for different logistics demand forecasting tasks for logistics demand researchers.

FORECASTING BUSINESS PROCESSES UNDER EXTREME CONDITIONS

The paper discusses the methodology of forecasting business processes, methods and their importance in extreme conditions of business process development. It has been shown that the main reason for the failure of business companies is incorrect management, which is mainly related to incorrect forecasting of the development of events and underestimation of expected risks. The attention is focused on qualitative, quantitative and expert methods of forecasting. Their features and specifics of use in extreme management conditions are analyzed. It is also about forecasting errors. Finally, a conclusion is drawn that it is preferable to use the combined forecasting methods in extreme management. Keywords: extreme situation, extreme management, forecasting the development of business processes, methodology and methods of forecasting.

Forecasting stock volatility with a large set of predictors: A new forecast combination method

AbstractThis paper investigates how to improve the prediction accuracy of stock realized volatility using a large set of predictors. Exploiting normalized positive adjusted and significant statistic of predictor obtained from the in‐sample result as weight, we develop two simple and effective forecast combination methods. Using an array of 86 equity, bond, forex, futures, behavior, macroeconomic, and uncertainty predictors, we find that the proposed methodologies significantly improve stock realized volatility out‐of‐sample prediction performance relative to several extant forecast combinations. This result is robust for different individual forecast models, different dependent variables, and different out‐of‐sample periods. Furthermore, we explain that out‐of‐sample predictability varies significantly with changes in the number of predictors. And the existence of a strongly powerful volatility predictor affects this change.

Is Single Forecast Method Better than Combined Forecast Method?

Many studies have been done to prove that combining forecast methods gives a better predictive performance relative to individual forecasts. This paper compared the single forecast method and the combined methods in predicting time series data. The study used annual oil revenue for the period 1981–2019 from the Central Bank of Nigeria (CBN), which were divided into two sets: the Training Set (TS) which covered the period 1981–2010 and the Test Set (VS) which covered 2011–2019. The study adopted autoregressive integrated moving average (ARIMA), simple exponential smoothing (SES), and Holt’s linear trend (Holt) as the individual forecast methods; it also adopted outperformance of forecasts (OPF) and weighted mean (WM) as weight selection methods. The forecast methods were applied to the Training Set after which they were combined. Two combined methods CM1 (ARIMA + SES) and CM2 (ARIMA + SES + Holt) were obtained. The result of this study showed that simple exponential smoothing (SES) as an individual forecast method is better and less risky than the combined methods for forecasting time series.

Two models for predicting stock prices in combination with LSTM

In addition to its practical and theoretical significance, stock forecasting has long been a hot research topic for scholars domestically and abroad. Stock data are time-series in nature, and neural networks have achieved relatively good performance in dealing with time series problems, among which long-short-term memory neural networks are well suited to dealing with such time-series data with long-term dependence. However, the stock market is an environment that changes with the external environment, with high stochasticity and complex intrinsic nonlinear relationships between different phenomena. Relying on a single method to identify the series directly cannot fully extract the complex information of the series changes, so the combined forecasting method is proposed. One idea is to combine empirical mode decomposition (EMD) with long-short-term memory (LSTM), and another idea is to incorporate LSTM or its variant GRU into generative adversarial networks (GAN/CGAN/WGAN). After an empirical study of Guanhao Bio's stock price and Apple's stock price, both methods present better prediction results.

Predicting energy futures high-frequency volatility using technical indicators: The role of interaction

In this paper, we investigate the predictability of technical indicators on energy futures volatility from the high-frequency and high-dimensional perspectives. We show that the technical indicators have significant impacts on crude oil and natural gas futures volatility based on in- and out-of-sample analysis. Further, we analyze the impacts of interactions among predictor variables on future volatility. Based on an improved conditional sure independence screening model, we find that the interactions contribute to the out-of-sample predictive power significantly. The improved model has robust and better forecasting performance relative to extant popular dimension reduction methods, forecast combination methods, and regularization methods. Moreover, we show that the out-of-sample predictability is robust during various periods. Finally, we show that technical indicators improve economic value in the crude oil market but the economic increment is not significant in the natural gas market.