The fluctuations of agricultural commodity prices have a great impact on people's daily lives as well the inputs and outputs production. An accurate forecast is therefore essential if authorities are to make scientific decisions. To more adaptively, this study proposes novel model selection framework which includes time series features horizons. Twenty-nine used depict three intelligent models specified candidate models; namely, artificial neural network (ANN), support vector regression...

Since 2017, the Financial Industry Regulatory Authority (FINRA) has shifted its focus to fewer but higher-impact cases targeting large, high-profile brokerage firms. Compared SEC, FINRA enforces a stricter penalty regime, with 80.9% of resolved within three years. This study also examines factors influencing FINRA's disciplinary actions, highlighting key regulatory priorities such as compliance registration requirements and asset management. Firms extensive misconduct histories high risks...

The electric power industry is the key to achieving goals of carbon peak and neutrality. Accurate forecasting emissions in can aid prompt adjustment generation policies early achievement reduction targets. This study proposes a new approach that combines decomposition-ensemble paradigm with knowledge distillation forecast daily emissions. First, Seasonal Trend decomposition using Locally weighted scatterplot smoothing (STL) used decompose data into three subcomponents. Second, two...

Compared with point forecasting, interval forecasting is believed to be more effective and helpful in decision making, as it provides information about the data generation process. Based on well‐established “linear nonlinear” modeling framework, a hybrid model proposed by coupling vector error correction (VECM) artificial intelligence models which consider cointegration relationship between lower upper bounds (Coin‐AIs). VECM first employed fit original time series residual modeled Coin‐AIs....

This paper proposes an improved ensemble empirical mode decomposition method based on genetic algorithm to solve the mixing problem in (EMD) as well parameters selection issue (EEMD) algorithm. In a (GA), orthogonality index is used formulate fitness function and Hamming distance specified design difference operator. By coupling GA with EEMD algorithm, higher efficiency generated, namely GAEEMD. Simulation experiment both intermittent signals sinusoidal verifies effectiveness robustness of...

Abstract Online search data provide us with a new perspective for quantifying public concern about animal diseases, which can be regarded as major external shock to price fluctuations. We propose modeling framework pork forecasting that incorporates online support vector regression model. This novel involves three main steps: is, formulation of the diseases composite indexes (ADCIs) based on data; forecast original ADCIs; and improvement decomposed ADCIs. Considering there are some noises...

Extracting hidden information embedded in the financial news is an effective approach to market volatility prediction. In this paper, we propose a recurrent neural network (RNN) based method that dynamically extracts latent structures from sequence of events for Specifically, first train skip-thought model on datasets represent semantic meaning sentences. Then aggregate representations single day form daily feature align with index. Finally, make use released some days ago better prediction,...

China’s livestock market has experienced exceptionally severe price fluctuations over the past few years. In this paper, based on well‐established idea of “forecast combination,” a forecast combination framework with different time scales is proposed to improve accuracy for products. Specifically, we combine forecasts from multi‐time scale, i.e., short‐term and long‐term forecast. Forecasts derived scale introduce complementary information about dynamics movements, thus increasing...

As CO2 emissions continue to rise, the problem of global warming is becoming increasingly serious. It important provide a robust management decision-making basis for reductions carbon worldwide by predicting accurately. However, affected various factors, prediction challenging due its nonlinear and nonstationary characteristics. Thus, we propose combination forecast model, named CEEMDAN-GWO-SVR, which incorporates multiple features predict trends in China’s emissions. First, impact online...

Forecasting agricultural commodity prices accurately is a challenging task due to the complexity of trading market and variability influencing factors. Many studies have proven that forecast combination an effective strategy for improving performance relative individual forecasting. In field combination, how determine reasonable weights still open question. This study proposed optimal framework forecasting, which integrates decomposition-reconstruction-ensemble methodology with improved...

Abstract The key to the accuracy of time series forecasting is find most appropriate method. Therefore, model selection has become a new research hotspot in data analysis field. However, existing methods reduce efficiency for relying on subjective manual features. In this paper, an automatic feature extraction framework proposed based idea meta learning. Inspired by computer vision, we transform one -dimensional into two-dimensional images, and use convolution neural network (CNN) train...

<title>Abstract</title> Accurate and reliable price forecasting of agricultural products is significant for promoting the production distribution products, optimizing resource allocation improving market efficiency. Due to non-stationary feature in price, based on decomposition integration kernel density estimation(KDE), this paper proposes a hybrid model that can quantify uncertainty potential forecasts by converting traditional point into interval forecasts. Firstly, sequence decomposed...

Vegetable is one of the necessities people's daily life. The quality and safety vegetable associated directly with health citizen affects sustainable development national economy. It Pesticide residue that important factors which vegetable. In this paper, Logistic model data mining technology applied to limited assay information forecasting risk pesticide in further. result credible can offer some decisions based on supervision supervisors.

Abstract In the analysis of agricultural product price time series, detection abnormal fluctuation is primary task. Accurately judging prices will give policy support to government and also assist farmers increase production income. A deep convolutional neural network model based on series image(TSI) introduced identify under improved standard deviation-Slope judgment. Markov Transfer Field(MTF) method used transform pre-processed sparse one-dimensional into two-dimensional dense images, a...