A Review and Analysis of Machine Learning and Statistical Approaches for Prediction

被引:0
|
作者
Nisha, K. G. [1 ]
Sreekumar, K. [1 ]
机构
[1] Coll Engn Poonjar, Dept Comp Sci & Engn, Poonjar, Kerala, India
关键词
ARIMA; Neural Network; Prediction; Support Vector Machine;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
India is the third largest natural rubber producing country of the world, next to Thailand and Indonesia, producing about 9 per cent of the global output. Kerala is the largest natural rubber producing state in India. Among the major plantation crops, natural rubber occupies a major role in agriculture income of Kerala state. The frequent up and down in the rubber price affect the daily life of those involved in the rubber production and consumption. Forecasting is the use of historic data to determine the direction of future trends. Accurate price forecasting of rubber will help the producers and consumers to avoid risk or loss in business. The objective of this survey is to do an experimental analysis for identifying a prediction model which can be used for predicting the Indian natural rubber price with better accuracy and minimum error.
引用
收藏
页码:135 / 139
页数:5
相关论文
共 50 条
  • [1] Statistical Machine Learning Approaches to Liver Disease Prediction
    Mostafa, Fahad
    Hasan, Easin
    Williamson, Morgan
    Khan, Hafiz
    [J]. LIVERS, 2021, 1 (04): : 294 - 312
  • [2] Machine learning approaches for neurological disease prediction: A systematic review
    Fatima, Ana
    Masood, Sarfaraz
    [J]. EXPERT SYSTEMS, 2024, 41 (09)
  • [3] A review of machine learning approaches for drug synergy prediction in cancer
    Torkamannia, Anna
    Omidi, Yadollah
    Ferdousi, Reza
    [J]. BRIEFINGS IN BIOINFORMATICS, 2022, 23 (03)
  • [4] Cardiovascular risk prediction: from classical statistical methods to machine learning approaches
    Sperti, Michela
    Malavolta, Marta
    Polacco, Federica Staunovo
    Dellavalle, Annalisa
    Ruggieri, Rossella
    Bergia, Sara
    Fazio, Alice
    Santoro, Carmine
    Deriu, Marco A.
    [J]. MINERVA CARDIOLOGY AND ANGIOLOGY, 2022, 70 (01) : 102 - 122
  • [5] Machine learning and statistical analysis for biomass torrefaction: A review
    Manatura, Kanit
    Chalermsinsuwan, Benjapon
    Kaewtrakulchai, Napat
    Kwon, Eilhann E.
    Chen, Wei-Hsin
    [J]. BIORESOURCE TECHNOLOGY, 2023, 369
  • [6] Statistical Machine Learning and Dissolved Gas Analysis: A Review
    Mirowski, Piotr
    LeCun, Yann
    [J]. IEEE TRANSACTIONS ON POWER DELIVERY, 2012, 27 (04) : 1791 - 1799
  • [7] Analysis of traditional machine learning approaches on heart attacks prediction
    Berdinanth, Micheal
    Syed, Samah
    Velusamy, Shudhesh
    Suseelan, Angel Deborah
    Sivanaiah, Rajalakshmi
    [J]. ROMANIAN JOURNAL OF INFORMATION TECHNOLOGY AND AUTOMATIC CONTROL-REVISTA ROMANA DE INFORMATICA SI AUTOMATICA, 2024, 34 (01): : 23 - 30
  • [8] Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction
    Santos, Daniel
    Saias, Jose
    Quaresma, Paulo
    Nogueira, Vitor Beires
    [J]. COMPUTERS, 2021, 10 (12)
  • [9] Machine Learning Approaches for Power System Parameters Prediction: A Systematic Review
    Makanju, Tolulope David
    Shongwe, Thokozani
    Famoriji, Oluwole John
    [J]. IEEE ACCESS, 2024, 12 : 66646 - 66679
  • [10] Early prediction of childhood asthma exacerbations through a combination of statistical and machine learning approaches
    Nagori, Aditya
    Sethi, Tavpritesh
    Kabra, Sushil Kumar
    Lodha, Rakesh
    Agrawal, Anurag
    [J]. EUROPEAN RESPIRATORY JOURNAL, 2020, 56