ERN: Neural Networks & Related Topics (Topic)最新文献_第2页

Impact of Near-Time Information for Prediction on Microeconomic Balanced Time Series Data using Different Machine Learning Methods 不同机器学习方法对近时信息对微观经济平衡时间序列数据预测的影响

ERN: Neural Networks & Related Topics (Topic) Pub Date : 2020-03-23 DOI: 10.2139/ssrn.3559645

Frederik Collin, M. Kies

引用次数: 0

Granger Causality Detection in High-Dimensional Systems Using Feedforward Neural Networks 基于前馈神经网络的高维系统Granger因果关系检测

ERN: Neural Networks & Related Topics (Topic) Pub Date : 2020-02-24 DOI: 10.2139/ssrn.3543687

Hector F. Calvo-Pardo, Tullio Mancini, Jose Olmo

{"title":"Granger Causality Detection in High-Dimensional Systems Using Feedforward Neural Networks","authors":"Hector F. Calvo-Pardo, Tullio Mancini, Jose Olmo","doi":"10.2139/ssrn.3543687","DOIUrl":"https://doi.org/10.2139/ssrn.3543687","url":null,"abstract":"Abstract This paper proposes a novel methodology to detect Granger causality on average in vector autoregressive settings using feedforward neural networks. The approach accommodates unknown dependence structures between elements of high-dimensional multivariate time series with weak and strong persistence. To do this, we propose a two-stage procedure: first, we maximize the transfer of information between input and output variables in the network in order to obtain an optimal number of nodes in the intermediate hidden layers. Second, we apply a novel sparse double group lasso penalty function in order to identify the variables that have the predictive ability and, hence, indicate that Granger causality is present in the others. The penalty function inducing sparsity is applied to the weights that characterize the nodes of the neural network. We show the correct identification of these weights so as to increase sample sizes. We apply this method to the recently created Tobalaba network of renewable energy companies and show the increase in connectivity between companies after the creation of the network using Granger causality measures to map the connections.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116488983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

機械学習・深層学習・計量経済学に関する先行研究の整理 (Previous Studies on Machine Learning, Deep Learning, and Econometrics) 机器学习、深度学习、计量经济学相关的先行研究整理(Previous Studies on Machine Learning, Deep Learning, and Econometrics)

ERN: Neural Networks & Related Topics (Topic) Pub Date : 2020-02-02 DOI: 10.2139/ssrn.3530417

T. Ishii

{"title":"機械学習・深層学習・計量経済学に関する先行研究の整理 (Previous Studies on Machine Learning, Deep Learning, and Econometrics)","authors":"T. Ishii","doi":"10.2139/ssrn.3530417","DOIUrl":"https://doi.org/10.2139/ssrn.3530417","url":null,"abstract":"Japanese Abstract: 本研究は、機械学習・深層学習・強化学習の分野と計量経済学の分野の近接性を探す点に特徴をもつ。計量経済学において時系列分析やパネル分析を学んでいれば、機械学習は分析手法とともに計量経済学の考え方に多くの共通点をもつことから今後は関係性をより体系的に考察することが重要となると考える。また計量経済学も因果推論の分野では機械学習が活用されており、その関係性を説明する。さらに機械学習と計量経済学の近年の研究の進展を概観するため、画像認識や音声認識における機械学習のビジネスへの応用やCATEやLASSO、政策配分問題としてのELMや疫学における因果推論としてCMA（因果媒介分析）・Causal Forest・SDD・構造推定などを幅広く扱う。 English Abstract: This research is characterized in that it seeks closeness between the fields of machine learning, deep learning and reinforcement learning and the field of econometrics. If you are studying time series analysis or panel analysis in econometrics, machine learning has much in common with econometrics as well as analytical methods, so it is important to consider relationships more systematically in the future. I think it will be. In econometrics, machine learning is used in the field of causal inference, and the relationship is explained. In order to give an overview of recent advances in research on machine learning and econometrics, we have applied machine learning to business in image recognition and speech recognition, CATE and LASSO, ELM as a policy distribution problem, and CMA (causal inference in epidemiology). Mediation analysis), Causal Forest, SDD, structure estimation, etc.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129248070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Economic Forecasting With Autoregressive Methods and Neural Networks 基于自回归方法和神经网络的经济预测

ERN: Neural Networks & Related Topics (Topic) Pub Date : 2020-01-17 DOI: 10.2139/ssrn.3521532

J. Chen

引用次数: 1

Bankruptcy Prediction of Banks: Neural Review of Literature 银行破产预测:文献神经学综述

ERN: Neural Networks & Related Topics (Topic) Pub Date : 2020-01-10 DOI: 10.2139/ssrn.3517172

Surbhi Dhama

引用次数: 0

Multi Scenario Financial Planning via Deep Reinforcement Learning AI 基于深度强化学习的多场景财务规划

ERN: Neural Networks & Related Topics (Topic) Pub Date : 2020-01-08 DOI: 10.2139/ssrn.3516480

Gordon Irlam

引用次数: 2

Artificial Intelligence in Asset Management 资产管理中的人工智能

ERN: Neural Networks & Related Topics (Topic) Pub Date : 2019-12-27 DOI: 10.2139/ssrn.3692805

Söhnke M. Bartram, J. Branke, Mehrshad Motahari

引用次数: 18

Accuracy of Financial Distress Model Prediction: The Implementation of Artificial Neural Network, Logistic Regression, and Discriminant Analysis 财务危机模型预测的准确性:人工神经网络、逻辑回归和判别分析的实现

ERN: Neural Networks & Related Topics (Topic) Pub Date : 2019-09-10 DOI: 10.2991/assehr.k.200529.084

Triasesiarta Nur, R. Panggabean

引用次数: 1

Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends 谷歌时尚:使用谷歌趋势预测时尚消费者行为

ERN: Neural Networks & Related Topics (Topic) Pub Date : 2019-04-04 DOI: 10.3390/SOCSCI8040111

E. Silva, Hossein Hassani, D. Madsen, Liz Gee

{"title":"Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends","authors":"E. Silva, Hossein Hassani, D. Madsen, Liz Gee","doi":"10.3390/SOCSCI8040111","DOIUrl":"https://doi.org/10.3390/SOCSCI8040111","url":null,"abstract":"This paper aims to discuss the current state of Google Trends as a useful tool for fashion consumer analytics, show the importance of being able to forecast fashion consumer trends and then presents a univariate forecast evaluation of fashion consumer Google Trends to motivate more academic research in this subject area. Using Burberry—a British luxury fashion house—as an example, we compare several parametric and nonparametric forecasting techniques to determine the best univariate forecasting model for “Burberry” Google Trends. In addition, we also introduce singular spectrum analysis as a useful tool for denoising fashion consumer Google Trends and apply a recently developed hybrid neural network model to generate forecasts. Our initial results indicate that there is no single univariate model (out of ARIMA, exponential smoothing, TBATS, and neural network autoregression) that can provide the best forecast of fashion consumer Google Trends for Burberry across all horizons. In fact, we find neural network autoregression (NNAR) to be the worst contender. We then seek to improve the accuracy of NNAR forecasts for fashion consumer Google Trends via the introduction of singular spectrum analysis for noise reduction in fashion data. The hybrid neural network model (Denoised NNAR) succeeds in outperforming all competing models across all horizons, with a majority of statistically significant outcomes at providing the best forecast for Burberry’s highly seasonal fashion consumer Google Trends. In an era of big data, we show the usefulness of Google Trends, denoising and forecasting consumer behaviour for the fashion industry.","PeriodicalId":114865,"journal":{"name":"ERN: Neural Networks & Related Topics (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134464041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 59

A Neural Network Boosted Double Over-Dispersed Poisson Claims Reserving Model 神经网络增强的双过分散泊松索赔保留模型

ERN: Neural Networks & Related Topics (Topic) Pub Date : 2019-04-04 DOI: 10.2139/ssrn.3365517

Andrea Gabrielli

引用次数: 10