以北海市为增长极:加快广西北部湾经济区信息产业发展

目前,我国增长极发展模式在环渤海湾地区、长江三角洲地区和珠江三角洲地区三个经济圈都取得了显著的经济效应.信息产业的发展,是国民经济和社会的信息化进程的发展的关键所在,也是广西区社会经济发展的迫切要求.本文通过SWOI分析广西北部湾经济区的北海市的信息产业现状,进一步对北海市作为广西北部湾经济区信息产业的核心增长极进行可行性分析,并提出建设广西北部湾信息产业区的构想.     

Forecasting with gradient boosted trees: augmentation,tuning, and cross-validation strategies: Winning solution to the M5 Uncertainty competition

Deep neural networks and gradient boosted tree models have swept across the field of machine learning over the past decade, producing across-the-board advances in performance. The ability of these methods to capture feature interactions and nonlinearities makes them exceptionally powerful and, at the same time, prone to overfitting, leakage, and a lack of generalization in domains with target non-stationarity and collinearity, such as time-series forecasting. We offer guidance to address these difficulties and provide a framework that maximizes the chances of predictions that generalize well and deliver state-of-the-art performance. The techniques we offer for cross-validation, augmentation, and parameter tuning have been used to win several major time-series forecasting competitions—including the M5 Forecasting Uncertainty competition and the Kaggle COVID19 Forecasting series—and, with the proper theoretical grounding, constitute the current best practices in time-series forecasting.     

Predictive analysis and modelling football results using machine learning approach for English Premier League

The introduction of artificial intelligence has given us the ability to build predictive systems with unprecedented accuracy. Machine learning is being used in virtually all areas in one way or another, due to its extreme effectiveness. One such area where predictive systems have gained a lot of popularity is the prediction of football match results. This paper demonstrates our work on the building of a generalized predictive model for predicting the results of the English Premier League. Using feature engineering and exploratory data analysis, we create a feature set for determining the most important factors for predicting the results of a football match, and consequently create a highly accurate predictive system using machine learning. We demonstrate the strong dependence of our models’ performances on important features. Our best model using gradient boosting achieved a performance of 0.2156 on the ranked probability score (RPS) metric for game weeks 6 to 38 for the English Premier League aggregated over two seasons (2014–2015 and 2015–2016), whereas the betting organizations that we consider (Bet365 and Pinnacle Sports) obtained an RPS value of 0.2012 for the same period. Since a lower RPS value represents a higher predictive accuracy, our model was not able to outperform the bookmaker’s predictions, despite obtaining promising results.     

新疆地州经济发展的梯度化差异研究

新疆地域广袤,区域经济发展差异较大,区域经济的差异已影响到新疆经济社会的可持续发展.研究新疆地州经济的区域差异,并根据差异现状进行分组,有利于新疆根据地州经济的梯度类型制定相应的倾斜政策,争取相应的对口支援,以促进新疆经济社会的均衡发展.本文借助方差分析的方法,结合逐步回归的思想,按经济发展水平和经济增长状况将新疆各地州经济差异水平客观地划分为五个梯度.划分结果显示：27％的地区属于经济发展水平较高的地区,33％的地区属于经济增长较快的地区,53％的地区属既属于经济发展水平较低区域,又属于经济增长较慢的地区,也即新疆各地州经济发展水平整体偏低,发展的均衡性较差;从发展态势来看,新疆各地州梯度差异将进一步扩大,动态上虽然会有所调整,但调整的结果不会显著.     

Interpretable sports team rating models based on the gradient descent algorithm

We introduce several new sports team rating models based on the gradient descent algorithm. More precisely, the models can be formulated by maximising the likelihood of match results observed using a single step of this optimisation heuristic. The proposed framework is inspired by the prominent Elo rating system, and yields an iterative version of ordinal logistic regression, as well as different variants of Poisson regression-based models. This construction makes the update equations easy to interpret, and adjusts ratings once new match results are observed. Thus, it naturally handles temporal changes in team strength. Moreover, a study of association football data indicates that the new models yield more accurate forecasts and are less computationally demanding than corresponding methods that jointly optimise the likelihood for the whole set of matches.     

A feature engineering approach to wind power forecasting: GEFCom 2012

This paper provides detailed information about team Leustagos’ approach to the wind power forecasting track of GEFCom 2012. The task was to predict the hourly power generation at seven wind farms, 48 hours ahead. The problem was addressed by extracting time- and weather-related features, which were used to build gradient-boosted decision trees and linear regression models. This approach achieved first place in both the public and private leaderboards.     

Gradient-based simulated maximum likelihood estimation for stochastic volatility models using characteristic functions

Parameter estimation and statistical inference are challenging problems for stochastic volatility (SV) models, especially those driven by pure jump Lévy processes. Maximum likelihood estimation (MLE) is usually preferred when a parametric statistical model is correctly specified, but traditional MLE implementation for SV models is computationally infeasible due to high dimensionality of the integral involved. To overcome this difficulty, we propose a gradient-based simulated MLE method under the hidden Markov structure for SV models, which covers those driven by pure jump Lévy processes. Gradient estimation using characteristic functions and sequential Monte Carlo in the simulation of the hidden states are implemented. Numerical experiments illustrate the efficiency of the proposed method.     

Land market,land development and urban spatial structure in Beijing

The paper first examines urban spatial patterns of the gradients of housing and land prices and land development intensity, and then tests the relationship between the land price gradient and housing price gradient. Urban theory predicts the former is steeper than the latter based on the notion of derived demand for land from the provision of housing services. Finally the paper examines the impact of the property of housing production function on urban spatial structure. For the property of housing production function, we are particularly interested in the elasticity of capital-land substitution. The paper concludes (1) market influences over spatial structure, (2) the derived demand for land, and (3) it is the actual (or expected) housing price increases that cause skyrocketing land prices, not the other way around.     

三峡工程动工三年来两坝间河道演变分析

根据葛洲坝运行以来的实测资料，首先分析了两坝之间达到冲淤平衡后河道的基本状况，然后重点分析了三峡工程动工以后两坝间河道的演变情况。认为三峡工程动工后，对两坝间 影响主要在：（１）施工中的填方和弃渣增大了河道的淤积量，增大了葛洲坝的库容损失；（２）弃渣使泥沙组成粗化，这有可能增大葛洲坝水轮机的磨损；（３）对航道的影响在三峡坝区主要是航道变窄，但航深仍能保持施工前的水平。     

共轭梯度法优化设计低噪声FET放大器

本文利用放大器的噪声系数及增益表达式,构成目标函数,再利用共轭梯度和三次插值法进行计算机优化。用编制的优化程序AMAD设计、制作了一个S波段低噪声放大器,达到了较为满意的指标,较好地满足了系统组件的要求。