Programmable Device Circuits and Systems

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Study of Temperature Data Analytics and to Analyze the Future Value Forecasting


Affiliations
1 Department of ISE, R.V. College of Engineering, Bengaluru, Karnataka-560059, India
     

   Subscribe/Renew Journal


Temperature analysis is the process of analyzing the temperature records to make a study on temperature behaviors, its variations etc.in different regions. These records are usually collected from the meteorological center. In this paper, the work carried out is on the data analytics of temperature. Temperature data analytics is performed on the data collected from meteorological center of India for a period of 20 years (1995-2014) of the Bengaluru station. The hidden pattern analysis is carried out on that data and also a forecasting model is built on it. A linear regression algorithm and ARIMA (Autoregressive Integrated Moving Average) algorithm is used to perform the forecasting. The qualifying results such as analysis on the factor that influences the temperature, the summarization of temperature of 20 years, prediction of next year data are performed. The paper gives a quick summarization of data of temperature which is easy to understand and helps the researcher to perform further analysis.


User
Subscription Login to verify subscription
Notifications
Font Size

  • Badhiye S. S, Dr. Chatur P. N, Wakode B. V., “Temperature and Humidity Data Analysis for Future Value Prediction: An Approach”, Government College of Engineering, Amravati, Maharashtra, India, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Volume 2, Issue 1, January 2012.

  • Ayham Omary, Ahmad Wedyan, Ahmed Zghoul, Ahmad Banihani, and Izzat Alsmadi, “An Interactive Predictive System for Weather Forecasting”, Computer science and IT Faculty Yarmouk University, Irbid, Jordan, Umm Al-Qura University , Salman bin Abdulaziz University, KSA, 978-1-4673-1550-0/12/$31.00 ©2012 IEEE

  • Chris Fraley, Adrian Raftery, Tilmann Gneiting, McLean Sloughter and Veronica Berrocal, “Probabilistic Weather Forecasting in R”, the R Journal Vol. 3/1, June 2011 ISSN 2073-4859

  • Iza Sazanita Isa, Saodah Omar, Zuraidi Saad, Norhayati Mohamad Noor, Muhammad Khusairi Osman, “Weather Forecasting Using Photovoltaic System and Neural Network”, Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM) Malaysia. 2010 Second International Conference on Computational Intelligence, Communication Systems and Networks. 978-0-7695-4158-7/10 $26.00 © 2010 IEEE DOI 10.1109/CICSyN.2010.63

  • Sara khan, Mohd Muqeem, Nashra Javed, Assistant Professor, “A Critical Review of Data Mining Techniques in Weather Forecasting”, Department of Computer Application, Integral University, Lucknow, India. International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 4, April 2016, DOI 10.17148/IJARCCE.2016.54266

  • Vaibhavi Mistry, Vibha Patel , “Weather Condition Prediction Using Semi-Supervised Data Mining Technique”, M.Tech Student, Dept. of Computer engineering, Uka Tarsadia University, Bardoli. Gujarat. INDIA. International Journal of Engineering Trends and Technology (IJETT) – Volume 20 Number 4, ISSN: 2231-5381 – Feb 2015.

  • Meghali A. Kalyankar Prof. S. J. Alaspurkar, “Data Mining Technique to Analyse the Metrological Data”, University of Amravati, Maharashtra, India. International Journal of Advanced Research in Computer Science and Software Engineering. ISSN: 2277 128X Volume 3, Issue 2, February 2013.

  • Javier Gutie ´ Rrez Illa ´ N, Chris D. Thomas, Julia A. Jones, Weng-Keen Wong, Susan M. Shirley And Matthew G. Betts, Global Change Biology (2014), “Precipitation and winter temperature predict long-term range-scale abundance changes in Western North American birds”, doi: 10.1111/gcb.12642.

  • S. Kotsiantis and et. al., “Using Data Mining Techniques for Estimating Minimum, Maximum and Average Daily Temperature Values”, World Academy of Science, Engineering and Technology 2007 pp. 450-454

  • Tasha R. Inniss “Seasonal clustering technique for time series data”, European Journal of Operational Research (175) 2006 pp. 376–384

  • J. Berrocal, Y. Gel, A. E. Raftery, and T. Gneiting. ProbForecastGOP: Probabilistic weather field forecast using the GOP method, 2010. URL http://CRAN. R- project.org/package=ProbForecastGOP. R package version 1.3.2.

  • V. J. Berrocal, A. E. Raftery, and T. Gneiting. Combining spatial statistical and ensemble information in probabilistic weather forecasts. Monthly Weather Review, 135:1386–1402, 2007.


Abstract Views: 291

PDF Views: 3




  • A Study of Temperature Data Analytics and to Analyze the Future Value Forecasting

Abstract Views: 291  |  PDF Views: 3

Authors

Sweta S. Konkanagaon
Department of ISE, R.V. College of Engineering, Bengaluru, Karnataka-560059, India
B. M. Sagar
Department of ISE, R.V. College of Engineering, Bengaluru, Karnataka-560059, India
N. K. Cauvery
Department of ISE, R.V. College of Engineering, Bengaluru, Karnataka-560059, India

Abstract


Temperature analysis is the process of analyzing the temperature records to make a study on temperature behaviors, its variations etc.in different regions. These records are usually collected from the meteorological center. In this paper, the work carried out is on the data analytics of temperature. Temperature data analytics is performed on the data collected from meteorological center of India for a period of 20 years (1995-2014) of the Bengaluru station. The hidden pattern analysis is carried out on that data and also a forecasting model is built on it. A linear regression algorithm and ARIMA (Autoregressive Integrated Moving Average) algorithm is used to perform the forecasting. The qualifying results such as analysis on the factor that influences the temperature, the summarization of temperature of 20 years, prediction of next year data are performed. The paper gives a quick summarization of data of temperature which is easy to understand and helps the researcher to perform further analysis.


References