Open Access
·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
• Information Sciences
• Optimization Sciences
• Control Theory
• Operational Research
• Decision Sciences
• Information Theory
• Information Technology
• Computer Networks and Communications
• Mathematical Programming
• Modelling and Simulation
• Database Management
• Applications to Engineering Sciences
• Applications to Technology
Issues up to 2022 co-published with and available at:
Consumer product prediction using machine learning
*P. AjithaCorresponding authorajitha.it@sathyabama.ac.inSchool of Computing Sathyabama Institute of Science and TechnologyDepartment of Information Technology Sathyabama Institute of Science and Technology Chennai, Tamil Nadu, 600119, IndiaView full profile →
, T. Tamilvizhitamilvizhi.phd.it@gmail.comDepartment of Computer Science and Engineering Panimalar Engineering CollegeDepartment of Computer Science and Engineering Panimalar Engineering College Chennai IndiaChennai, Tamil Nadu, IndiaView full profile →
, K. Naga Sowjanyasowjanyakasi1@gmail.comDepartment of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering CollegeChennai, Tamil Nadu, IndiaView full profile →
, R. Surendransurendran.phd.it@gmail.comDepartment of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai, Tamil Nadu, IndiaView full profile →
, Bhoomeshwar Baladrbhoom08@dtu.edu.etDepartment of Computer Science Debre Tabor UniversityDebra Tabor, Debra Tabor, EthiopiaView full profile →
* Corresponding author · click or hover a name for details
Time-series forecasting is an approach that uses historical and current data to project future values over time or at a given point in time, while forecasting and prediction are often synonymous, there is one interesting detail. In some professions, forecasting may refer to data at a specific future point in time, whereas prediction refers to future data in general. Most widely used to determine the nature of stock prices. A series of analyses and modeling by a finance committee is to guide investors, professors of legal sciences, and processes. And that is why he proposes that this series argument not include a sliding window; they were wise to back then, and they gave up everything, anticipating stock values relative to her. The system presents the (GUI) Graphical User Interface as a stand-alone application. The proposed findings demonstrate a highly predicted accurate approach for nonlinear time series models that are difficult to obtain from traditional models.
[1] Fatima, S., Uddin, M. On the forecasting of multivariate financial time series using hybridization of DCC-GARCH model and multivariate ANNs. Neural Comput & Applic, 34, pp. 2191121925 (2022). [2] Rahul Maheshwari, Vivek Kapoor. Estimating the volatility of stock price index for Indian market using GARCH model. Journal of Statistics and Management Systems 25:7, pages 1523-1530 (2022).[3] Tamilvizhi, T., Surendran, R., Carlos Andres, Tavera Romero., Sadish Sendil, M. Privacy Preserving Reliable Data Transmission in Cluster Based Vehicular Adhoc Networks, Intelligent Automation & Soft Computing, vol. 34, no. 2, pp. 1265-1279 (2022).[4] Yu, E. Comparative Analysis of ARIMA Model and Neural Network in Predicting Stock Price. Big Data Analytics for Cyber-Physical System in Smart City. vol 1117. Springer, Singapore (2019). [5] Dutta S, and Rohit R, “Stock market prediction using data mining techniques with R”, Int. J. Eng. Sci. Comput., Vol. 7, No. 3, pp. 5436-5441 (2017). [6] Sakalauskas, V., Kriksciuniene, D. Entropy-Based Indicator for Predicting Stock Price Trend Reversal. 2011. Lecture Notes in Business Information Processing, vol 97. Springer, Berlin, Heidelberg (2011).[7] Sethi, J.K. and Mittal, M. A new feature selection method based on machine learning technique for air quality dataset. Journal of Statistics and Management Systems, 22(4), pp.697-705 (2019).[8] Tamilvizhi, T., Surendran, R., Anbazhagan, K., Rajkumar, K. Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification, Mathematical Problems in Engineering, 2022, 3452413 (2022).[9] Tharani, K., Kumar, N., Srivastava, V., Mishra, S. and Pratyush Jayachandran, M. Machine learning models for renewable energy forecasting. Journal of Statistics and Management Systems, 23(1), pp.171-180 (2020).[10] Suman, S.K. and Hooda, N., Predicting risk of Cervical Cancer: A case study of machine learning. Journal of Statistics and Management Systems, 22(4), pp.689-696 (2019).[11] Gyana Ranjan Patra, Mihir Narayan Mohanty. An LSTM-GRU based hybrid framework for secured stock price prediction. Journal of Statistics and Management Systems, 25:6, pages 1491-1499 (2022).
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