Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

被引:4
作者
Mohan, M. [1 ]
Raja, P. C. Kishore [2 ]
Velmurugan, P. [3 ]
Kulothungan, A. [4 ]
机构
[1] SRM Univ, Dept Comp Sci & Engn, Sonepat, Haryana, India
[2] SRM Univ, Dept Elect & Commun Engn, Sonepat, Haryana, India
[3] SRM Inst Sci & Technol, Dept Comp Technol, Chennai, Tamil Nadu, India
[4] SRM Inst Sci & Technol, Dept Comp Sci & Engn, Modinagar, Ghaziabad, India
关键词
Stock market; stock market prediction; time series forecasting; efficient market hypothesis; National stock exchange India; smoothing; observation; trend level; seasonal factor;
D O I
10.32604/iasc.2023.026255
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss. The proposed model uses a real time dataset of fifteen Stocks as input into the system and based on the data, predicts or forecast future stock prices of different companies belonging to different sectors. The dataset includes approximately fifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not; the forecasting is done for the next quarter. Our model uses 3 main concepts for forecasting results. The first one is for stocks that show periodic change throughout the season, the ???Holt-Winters Triple Exponential Smoothing???. 3 basic things taken into conclusion by this algorithm are Base Level, Trend Level and Seasoning Factor. The value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters Algorithm. The second concept is ???Recurrent Neural Network???. The specific model of recurrent neural network that is being used is Long-Short Term Memory and it???s the same as the Normal Neural Network, the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback loop. The third concept is Recommendation System which filters and predict the rating based on the different factors.
引用
收藏
页码:1151 / 1163
页数:13
相关论文
共 50 条
[31]   Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction [J].
Chung, Hyejung ;
Shin, Kyung-shik .
NEURAL COMPUTING & APPLICATIONS, 2020, 32 (12) :7897-7914
[32]   An Advisor Neural Network framework using LSTM-based Informative Stock Analysis [J].
Ricchiuti, Fausto ;
Sperli, Giancarlo .
EXPERT SYSTEMS WITH APPLICATIONS, 2025, 259
[33]   Mapping stock market dynamics: A tripartite neural network approach using modified grid search for stock market prediction [J].
Singh, Sachin ;
Singh, Mohinder ;
Attri, Shradha .
EXPERT SYSTEMS WITH APPLICATIONS, 2025, 278
[34]   Competitive feedback particle swarm optimization enabled deep recurrent neural network with technical indicators for forecasting stock trends [J].
Vanguri, Nagarjun Yadav ;
Pazhanirajan, S. ;
Kumar, T. Anil .
INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS, 2023, 7 (02) :385-405
[35]   Competitive feedback particle swarm optimization enabled deep recurrent neural network with technical indicators for forecasting stock trends [J].
Nagarjun Yadav Vanguri ;
S. Pazhanirajan ;
T. Anil Kumar .
International Journal of Intelligent Robotics and Applications, 2023, 7 :385-405
[36]   A Prescriptive Stock Market Investment Strategy for the Restaurant Industry using an Artificial Neural Network Methodology [J].
Weckman, Gary R. ;
Dravenstott, Ronald W. ;
Young, William A., II ;
Ardjmand, Ehsan ;
Millie, David F. ;
Snow, Andy P. .
INTERNATIONAL JOURNAL OF BUSINESS ANALYTICS, 2016, 3 (01) :1-21
[37]   Sentence Level Sentimental Analysis with Neural Network Using RSS News Feed on Stock Market Informations [J].
Sarma S.L.V.V.D. ;
VenkataSekhar D. ;
Murali G. .
SN Computer Science, 4 (5)
[38]   STOCK MARKET PREDICTION IN BRICS COUNTRIES USING LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK HYBRID MODELS [J].
Ataman, Gorkem ;
Kahraman, Serpil .
SINGAPORE ECONOMIC REVIEW, 2022, 67 (02) :635-653
[39]   A deep and wide neural network to predict summer monsoon rainfall using time series data [J].
Bajpai, Vikas ;
Bansal, Anukriti ;
Dash, Subrat .
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2023, 35 (08)
[40]   Performance Analysis of Different Recurrent Neural Network Architectures and Classical Statistical Model for Financial Forecasting: A Case Study on Dhaka Stock Exchange [J].
Bhowmick, Akash ;
Rahman, Asifur ;
Rahman, Rashedur M. .
ARTIFICIAL INTELLIGENCE METHODS IN INTELLIGENT ALGORITHMS, 2019, 985 :277-286