Forecasting Techniques for Sales Prediction
V.Phanindra Reddy1, N.Lokesh Kumar2, P.Krishna Kanth3, P.S.V.S.Sridhar4
1V.Phanindra Reddy, student, Department of computer science and engineering, K L Educational foundation, Deemed to be University, Vaddeswaram, Andhra Pradesh, India.
2N.Lokesh Kumar, student, Department of computer science and engineering, K L Educational foundation, Deemed to be University, Vaddeswaram, Andhra Pradesh, India.
3P.Krishna Kanth, Student, Department of computer science and engineering, K L Educational foundation, Deemed to be University, Vaddeswaram, Andhra Pradesh, India.
4P.S.V.S.Sridhar, Associate Professor, Department of Computer Science and Engineering, K L Educational foundation, Vaddeswaram, Andhra Pradesh, India.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4824-4927 | Volume-8 Issue-5, January 2020. | Retrieval Number: E5973018520/2020©BEIESP | DOI: 10.35940/ijrte.E5973.018520
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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: All of us are very curious about future, very excited to know what will happen in the next moment. Similarly, retailers are also curious about the future of their business, its progress and their future sales. Walmart is the world’s biggest retailer and also has a vast grocery chain over the world. It was initially established in America 1962. In 2019, it has more than 11,000 stores in 28 countries but the sales differ from place to place. Many sales strategies, discount rates will be introduced for the improvement of sales. Retailers always try to attract the common people to visit their store. They always focus on improving the future sales. Using some Machine learning forecasting models, we can estimate the future sales based on the past data. Our aim is to apply time series forecasting models to retail sales data, which contains weekly sales of 45 Walmart stores across United States from 2010 to 2012. There are other factors which effects the analysis of weekly sales – markdown, consumer per index, Is Holiday (boolean value returns whether it is holiday or not), size of the store, unemployment, store type, fuel price and temperature. The forecasting models applied for the data are Autoregressive Integrated Moving Average (ARIMA) model and Feed Forward Neural Networks (FFNN). The dataset will be divided into training and testing datasets. The predicted values will be checked with the test data and accuracy will be calculated. Based on the accuracy we conclude which of the two models will better for the sales prediction.
Keywords: ARIMA, FFNN, ML, AI.
Scope of the Article: Software Engineering Techniques and Production Perspectives.