Author : Mohammed Farooq Abdulla FM 1
Date of Publication :14th May 2022
Abstract: In recent years development of computer systems were able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data is known as machine learning.In this phase sales of different lubricants were predicted using a multivariate time series forecasting algorithm.Previously it showed that the model was accurate in predicting the engine oil sales for a particular time.Using Regressions the accuracy of sales prediction was less (74%) and the models like SVM and Random forest were showing signs of over fitting.The accuracy obtained in the multivariate time series forecasting was good than other algorithms.Time series algorithms are used extensively for forecasting time-based data.In time series ARIMA,SARIMA and SARIMAX are the common methods to forecast time based data.SARIMAX are efficient in forecasting data which has seasonality trends than ARIMA which are good in forecasting data which is stationary in nature Time series methods are extensively used for forecasting time based data.In time series ARIMA,SARIMA and SARIMAX are the common methods to forecast tie based data.ARIMA is the abbreviation of Auto Regressive Integrated Moving Average a model which explains a given time series model based on its lags and other values.SARIMAX is the abbreviation of Seasonal Auto Regressive Integrated Moving Average with Xegeneous variables. ARIMA model is best for forecasting stationary time series data and SARIMAX is used for forecasting values which is seasonal in nature.
Reference :
-
- Prapanna Mondal,Labani Shit and Saptarsi Goswami-”Study Of Effectiveness Of Time Series Modeling (ARIMA) In Forecasting Stock Prices”, International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.4, No.2, April 2014:14-29 pp 13-24
- Xin James He-”Crude Oil Price Prediction Time Series vs SVR Models-”Journal of International Technology and Information Management,Volume 27 Issue 2 January 2018.pp-25-42.
- ShengweiWang,Juan Feng,Gang Liu, Application Of Seasonal time series model in the precipitation forecast” on Mathematical and Computer modeling,Volume 58,Issue 3-4 on August 2013 pp 68-73.
- Abubakar Alkali, Ibrahim Atan Bin Sipan, Muhammad Najib Razali-”The Impact Of Macroeconomic Variables On Real Estate Price Forecasting Modelling In Abuja Nigeria “International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-8 Issue-5C, May 2019,pp-67-72.
- Bohdan M. Pavlyuchenko-”Machine-Learning Models for Sales Time Series Forecasting”from 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, 21–25 August 2018,Data 2019, 4, 15,pp 1-11.
- Aarati Gangshetty,Gurpreet Kaur,Uttam Sitaram Malunje-”Time Series Prediction of Temperature in Pune using Seasonal ARIMA Model” fromInternational Journal of Engineering Research & Technology (IJERT) Vol. 10 Issue 11, November-2021,pp 235-240.
- N Viswam and G Satyanarayana Reddy-”Stock market prediction using time series analysis”,International Journal of Statistics and Applied Mathematics on 2018 ISSN: 2456-1452 in December 2017,pp 465-469.
- Soham Talukdar-”Application energy prediction using time series forecasting:A Comparative study of different Machine learning algorithms”,International trends of Advanced Trends in Computer Applications Volume 08 Number 01,February 2021,pp 7-18.
- Shambulingappa-”Crude Oil Price Forecasting Using Machine Learning” International Journal of Advanced Scientific Innovation Volume 01 Issue 01 2020 ISSN 2582-8436 pp 6-11.
- S. Vasantha Kumar,Lelitha Vanajakshi-”Short-term traffic flow prediction using seasonal ARIMA model with limited input data”,in European Transport Research Review,Volume 7,Article number 21,June 2015,pp 1-9 .
- Navya Sri Kalli , Harsha Teja Pullagura -”Predicting Total Business Sales using Time Series Analysis” International Journal of Scientific Research in Computer Science,Engineering and Information Technology ISSN:2456-3307 ,Volume 6, Issue 4 pp-475-482
- Peng Chen,HongYong Yuan,Xueming Shu-”Forecasting crime using ARIMA model”,published on November 2008 .ISBN:978-0-7695-3305-6 in Fifth International Conference on Fuzzy Systems and Knowledge Discovery in November 2008,pp:627-630.
- Nari Sivanandam Arunraj,Diane Ahrens,Michael Fernandes“Applications of SARIMAX models to forecast daily sales in Food Industry”,International Journal of Operations Research and Information Systems ,April 2016,Volume 7,Issue 2,pp 1-21.
- Catherine McHugh,Sonya Coleman,Dermott Kerr,Daniel McGlynn ,”Forecasting Day-ahead Electricity Prices with SARIMAX,Vol 1,International Conference on Time Series and Forecasting,Volume l, 27 Sep 2019,pp-310-320.
- Adhistya Erna Permanasari,Indriana Hidayah, Isna Alfi Buston,”SARIMA (Seasonal ARIMA) Implementation on Time Series to Forecast The Number of Malaria Incidence”, on International Conference on Information Technology and Electrical Engineering,ICITEE 2013,pp 203-207.