Food Predictive Anatomization using Time Series
N. Kumaran1, P. Surya Sampath2, P. Saran Satya Kumar3
1Mr. N. Kumaran, Associate Professor, Dept. of Computer Science Engineering, SCSVMV, Deemed University, Kanchipuram, India.
2P. Surya Sampath, UG Student, Dept. of Computer Science Engineering, SCSVMV, Deemed University, Kanchipuram, India.
3P. Saran satya kumar, UG Student, Dept. of Computer Science Engineering, SCSVMV, Deemed University, Kanchipuram, India.
Manuscript received on June 10, 2020. | Revised Manuscript received on June 23, 2020. | Manuscript published on July 10, 2020. | PP: 14-17 | Volume-9 Issue-9, July 2020 | Retrieval Number: 100.1/ijitee.G5610059720 | DOI: 10.35940/ijitee.G5610.079920
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Time series survey and forecasting upcoming values has been a research focus past years ago. Time series analysis and predict The time-series data finds its importance in various roles of implementation such as business, stock market exchange, weather forecasting, electricity demand, cost and usage of products such as fuels, etc. In this project, a detailed survey of the various techniques applied for forecasting different method of time series datasets are provided. Moving average model and Auto-Regressive Integrated Moving Average model with a case study on food predictive analysis time series data with R software.
Keywords: Moving average model, ARIMA Model, Augmented Dickey-Fuller Test, Kwiatkowski-Phillips Schmidt-Shin test.
Scope of the Article: Surveying