TAILIEUCHUNG - Application of time series models in forecasting automobile sectors volatility for selected period.

This paper suggested a review on some of the most crucial works gives a meticulous view of recent machine learning (ML) techniques in the quantitative share price prediction showing that these are the methods transcend some traditional approaches. | Application of time series models in forecasting automobile sectors volatility for selected period. International Journal of Management IJM Volume 11 Issue 4 April 2020 pp. 5 14 Article ID IJM_11_04_002 Available online at http ijm JType IJM amp VType 11 amp IType 4 Journal Impact Factor 2020 Calculated by GISI ISSN Print 0976-6502 and ISSN Online 0976-6510 IAEME Publication Scopus Indexed APPLICATION OF TIME SERIES MODELS IN FORECASTING AUTOMOBILE SECTORS VOLATILITY FOR SELECTED PERIOD. Ajay S. Ghangare Assistant Professor DMT Department of Management Technology Shri Ramdeobaba College of Engineering and Management Ramdeobaba Tekdi Gittikhadan Katol Road Nagpur 440013 India Tanmay Gupta Assistant Professor DMT Department of Management Technology Shri Ramdeobaba College of Engineering and Management Ramdeobaba Tekdi Gittikhadan Katol Road Nagpur 440013 India Mr. Shubham Singh Student Shri Ramdeobaba College of Engineering and Management Ramdeobaba Tekdi Gittikhadan Katol Road Nagpur 440013 India ABSTRACT The Bombay Stock Exchange is extensive and fully regulated trading system in India. Exploration and forecasting of stock market time series data have developed considerable interest from the researchers over the last decade. Time Series modelling techniques perform pivotal role in prediction of data for future demands. Volatility forecasting has become crucial for investors policy holders retailers since bringing preciseness in estimating the future is very difficult. Automotive sector has gone through severe crash in their operations in last decade mostly due to policy changes policy paralysis and confusion among retailers about several new changes to be brought by the authority. This paper suggested a review on some of the most crucial works gives a meticulous view of recent machine learning ML techniques in the quantitative share price prediction showing that these are the methods transcend some traditional .

• Tự động hoá
• Machine learning techniques
• Quantitative share price prediction
• S uitability of ARIMA model
• Time series analysis
• TIME SERIES MODELS
• Machine learning
• Nonlinear autoregressive
• Long short term Memory
• Based on the available data
• Environmental advances
• Environmental contaminant mixtures
• Thyroid hormones
• Principal component analysis
• Ebook Data Mining Practical
• Machine Learning Tools
• Data Mining Practical
• Weka machine learning workbench
• Introduction to Weka
• Extensions and applications
• Oral cancer prognosis
• Feature selection
• Handling noisy
• Using Machine Learning
• Techniques to Build
• Comma Checker for Basque
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Extracting Comparative Sentences
• Korean Text Documents Using Comparative Lexical Patterns
• báo cáo ngôn ngữ
• Using Emoticons to reduce Dependency
• Sentiment Classification
• Techniques to Interpret WH questions
• Ingrid Zukerman
• Multilayer perceptron (MLP) network
• Radial basis function (RBF) network
• Consumers’ purchase behaviour
• Forecasting Vietnamese consumers’ purchase behaviour
• BMC Bioinformatics
• Genome wide association
• GWAS techniques
• Age related macular degeneration
• Financial risks
• Support vector machine
• Decision tree
• Naïve Bayes
• Missing data characteristics
• The choice of imputation techniques
• Extreme learning machine
• Generate complete datasets
• Monotone missing pattern
• Random forest
• Imbalanced learning
• Genome analysis
• Pseudo hairpins
• báo cáo hóa học
• công trình nghiên cứu về hóa học
• tài liệu về hóa học
• cách trình bày báo cáo
• Venous Thromboembolism
• Certain Groups of Patients
• Neonates
• Children and Patients
• Chronic Renal
• Use Case Scenarios
• Canonical Processes
• Semantic Web Languages
• Multimedia Analysis
• Semantically Annotated
• Dimension Reduction
• Data Clustering Techniques
• Max Margin Classifications
• Discriminative Graphical Models
• Hidden Markov Models
• Generative Graphical Models
• Deep neural networks
• Functional annotations
• Infectious disease associated host genes
• Sequence and interaction network features
• Insertions and deletions
• InDel detection
• Biological experiments
• Soybean species
• BMC Musculoskeletal Disorders
• ehavioural change techniques
• Chronic disease
• Hip osteoarthritis
• Hệ thống phát hiện xâm nhập (IDS)
• Những phương pháp máy học
• Intrusion Detection System (IDS)
• Denial of Service attack (DoS)
• Probe attack (Probe)
• Short reads
• Di nucleotide associations
• Threshold value
• DNA pooling
• Despite ongoing reduction
• Genotyping costs
• Clustering techniques