The application of time series forecasting utilizing historical data has become increasingly essential across a variety of industries including finance, healthcare, meteorology, and industrial sectors. The assessment of bond transaction rates in the interbank bond market serves as a crucial indicator for assessing bank risk. In this paper, we proposed a composite model to forecast the transaction interest rates of China’s interbank bonds over a long period. Specifically, our model integrates an intrinsic complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) model along with various long-term prediction models including long short-term memory network, temporal convolutional network, transformer, and autoformer. Our findings reveal that: 1) predictive performance of different long-term prediction models varies across different frequencies of single time series data; 2) predictive efficacy of diverse model combinations differs across varying prediction time lengths; 3) best results can be realized by using different prediction model combinations for high-frequency, medium-frequency and low-frequency data under different time steps.

Long-Term Interbank Bond Rate Prediction Based on ICEEMDAN and Machine Learning

The Vehicle Routing Problem (VRP) is related to determining the route of several vehicles to distribute goods to customers efficiently and minimize transportation costs or optimize other objective functions. VRP variations will continue to emerge as manufacturing industry production distribution problems become increasingly complex. Meta-heuristic methods have emerged as a powerful solution to overcome the complexity of VRP. This article provides a comprehensive review of the use of meta-heuristic methods in solving VRP and the challenges faced. A review of popular meta-heuristic methods is presented, including Simulated Annealing, Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization. The advantages of each method in solving the VRP and its role in solving complex distribution problems are discussed in detail. Challenges that may be encountered in using meta-heuristics for VRPs are analyzed, along with strategies to overcome these challenges. This article also recommends further research that includes adaptation to more complex VRP variants, incorporation of meta-heuristic methods, parameter optimization, and practical implementation in real-world scenarios. Overall, this review explains the important role of meta-heuristic methods as intelligent solutions to increasingly complex distribution and logistics challenges. 

Challenges and Opportunities for Applying Meta-Heuristic Methods in Vehicle Routing Problems: A Review

http://www.cell.com/article/S2405844023038653/pdf

Do foreign investors affect the volatility of local currency bond prices? Empirical evidence from China

Aiming at the vehicle routing problem with time window constraints, this paper discusses the vehicle routing optimization problem among multiple warehouses, multiple products and multiple customers. In this paper, a multi-objective model of vehicle path optimization based on the improved simulated annealing algorithm is established by adding memory function and setting monotonous heating up and other real-time optimization strategies. The optimization objectives include total transportation cost, total driving distance and total driving time. In order to prove the effectiveness of the proposed model, the optimization results of standard genetic algorithm, random search and the algorithm in this paper are compared on the randomly generated dataset. The simulation results show that the algorithm designed in this paper is fast and efficient in all four cases.

Multi-objective vehicle routing problem with time windows based on improved simulated annealing algorithm

The Moderate Resolution Imaging Spectroradiometer (MODIS) has 36 channels covering the visible to far infrared band range with a resolution of 250 m to 1 km, and is important for detecting fires in large areas. Traditional fire detection algorithms mainly rely on thermal infrared channels using threshold or contextual methods. Such methods usually fail to detect small fire and often misidentify high-temperature objects on the surface. This paper proposes a new adaptive fire detection algorithm, which focuses on two methods to improve the accuracy of fire detection. First, single-channel and multi-channel test conditions were added and new contextual algorithms were adopted; second, a method for weighting the fire test conditions based on the test conditions for differences in the sensitivity to fire was proposed. This method reduces the issue of small fires being overlooked because they do not satisfy certain test conditions. In addition, a priori database was built using twelve-year US wildfire reference records and highest confidence fire data in MODIS fire products, adaptive thresholds suitable for fires were selected using the bubble sorting method based on the radiation characteristics of global fires. Testing results show that the improved algorithm improved the accuracy of small fire identification and reduced the false detection rate of pseudo-fires.

Improved algorithm for adaptive fire detection using MODIS data

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Do foreign investors affect the volatility of local currency bond prices? Empirical evidence from China

Multi-objective vehicle routing problem with time windows based on improved simulated annealing algorithm

Improved algorithm for adaptive fire detection using MODIS data