A Pragmatic Study on Time Series Models for Big Data

B. Arputhamary, L. Arockiam

Abstract


Recent years have witnessed the growth of Big Data, particularly Time Series data which initiates major research interest in Time Series analysis and forecasting future values. It finds interest in many applications such as business, stock market and exchange, weather forecasting, electricity demand, cost and usage of products and in any kind of place that has specific seasonal or trendy changes over time. The forecasting of Time Series data provides the organization with useful information that is necessary for making important decisions. In this paper, a detailed study is performed to find the total number of bike users with respect to the season and weather on Capital Bike Sharing System (CBS) dataset. The study covers the Auto Regressive Integrated Moving Average (ARIMA), Holt-Winters Additive and Multiplicative forecasting models to analyse the seasonal and trendy fluctuations of the given dataset to improve performance and accuracy.

Full Text:

PDF

References


R. K. Agrawal, “An Introductory Study on Time SeriesModeling and Forecasting”,LAP Lambert Academic Publishing, Germany, pp 1-67, 2013.

Hipel K.W. and McLeod A.I., “Time Series Modelling of Water Resources and Environmental Systems”, 2005.

G.P. Zhang, “A neural network ensemble method with jittered training data for Time Series forecasting”, Information Sciences, pp 5329-5346, Volume 177, Issue 23, 2007.

Rob J. Hyndman and YeasminKhandakar, “Automatic Time Series Forecasting: The forecast Package for R”, Journal of Statistical Software (JSS),pp 1-22, Volume 27, Issue 03, 2014.

S.Hamm, “How Big Data can Boost Weather Forecasting”, 2013.

LiljanaFerbarTratar, “Improved Holt Winters Method:A Case of Overnight stays of Tourists in Republic of Slovenia”, Economic and Business Review , 2013.

Aditi Jain, ManjuKaushik, “Performance Optimization in Big Data Predictive Analytics”,International Journal of Advanced Research in Computer Science and Software Engineering( IJARCSSE), ISSN: 2277 128X , pp 126-129, Volume 04, Issue 08, 2014.

Ekaterina Gonina, AnithaKannan, John Shafer, MihaiBudiu, “Parallelizing large-scale data processing applications with data skew: a case study in product offer matching", International Workshop on MapReduce and its Applications,2011.

Min Chen, Shiwen Mao, Yunhao Liu, “Big Data: A SurveyMobile Networks and Applications, The Journal of Special Issues on Mobility of Systems, ISSN: 1383-469X (Print) 1572-8153 (Online),”, pp 171-209, Volume 19, Issue 02,Springer, 2014.

Lei Li, FarzadNoorian, Duncan J.M. Moss, Philip H.W. Leong, “Rolling Window Time Series Prediction Using MapReduce”, 15th International Conference on Information Reuse and Integration (IEEE IRI 2014),ISBN: 978-1-4799-5879-5,pp 1-4, 2006.

Sachchidanand Singh, Nirmala Singh, “Big Data Analytics”, International Conference on Communication, Information and Computing Technology(ICCICT),ISBN : 978-1-4577-2078-9, Oct 19-20, 2012.

Dilpreet Singh and Chandan K Reddy, “ A survey on platforms of Big Data Analytics”, Journal of Big Data, Springer Open Access, Volume 02, Issue 08,2014.

RashmiRanjanDhall and B.V.A.N.S.S. Prabhakar Rao, “ Shrinking the Uncertainty In Online Sales Precdiction With Time Series Analysis”, Journal on Soft Computing(ICTACT), pp 869-874,Volume 05, Issue 01, 2014

B. Arputhamary, L.Arockiam, R.ThamaraiSelvi, “Analysis of Prediction Techniques in Time Series for Big Data Using R”, International Conference on Engineering Technology and Science(ICETS’15),ISSN 0973-4562, pp 6712-6715, Volume 10, Issue 09, 2015.

B. Arputhamary, L.Arockiam, “Parallel Prediction Model for Big Data using MapReduce Programming Model”, International Journal of Applied Engineering Research, ISSN 0973-4562, Volume 10, Issue 82, 2015.

B. Arputhamary, L. Arockiam, “Improved Time Series Based Algorithm for Big Data using MapReduce Programming Model”, International Journal of Applied Engineering Research, ISSN 0973-4562, Volume 10, Issue 85, 2015.

Kanagalakshmi R, “Big Data: Performance Analysis of Vendor and Value Creation through Big Data Analytics”, International Journal of Engineering Sciences and Research Technology (IJESRT), ISSN: 2277-9655, Volume 03, Issue 12, 2014, pp 429-434.




DOI: https://doi.org/10.23956/ijermt.v6i8.120

Refbacks

  • There are currently no refbacks.