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Open Access
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Kanishk Patil, Anuj Sawant, Shantanu Rane, Siddhant Pupulwad, DK Chitre
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- Student, Student, Student, Student, Assistant Professor Department of computer engineering, Terna Engineering College, Navi Mumbai, Department of computer engineering, Terna Engineering College, Navi Mumbai, Department of computer engineering, Terna Engineering College, Navi Mumbai, Department of computer engineering, Terna Engineering College, Navi Mumbai, Department of computer engineering, Terna Engineering College, Navi Mumbai Maharashtra, Maharashtra, Maharashtra, Maharashtra, Maharashtra India, India, India, India, India
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Abstract
nSales forecasting is a critical application in various industries and presents one of the most challenging problems worldwide. One method of prediction involves identifying patterns in historical data, where the outcome is known in advance and can be validated using more recent data. If a pattern consistently leads to the same outcome, it can be considered a genuine relationship. This method is highly flexible and can be utilized with diverse datasets, extending beyond climate data. Sales prediction involves numerous parameters such as the number of sales, production levels, associated costs, and time requirements, which are challenging to quantify and measure accurately. “Time Series Sale Forecasting Using ARIMA Model” presents a novel methodology for sales forecasting, specifically tailored to analyze seasonal sales data. The study extensively examines monthly sales data spanning multiple years, with a particular emphasis on identifying inherent seasonality within the dataset. An essential element of the methodology is the utilization of the Augmented Dickey-Fuller (ADF) test to confirm the stationarity of the time series data, establishing a strong basis for precise forecasting. At the core of the study is the sole application of the Autoregressive Integrated Moving Average (ARIMA) model for sales prediction. This model is renowned for its adaptability and flexibility, proving highly effective in capturing intricate patterns embedded within the sales data. By leveraging the ARIMA model’s capabilities, the study demonstrates its efficacy in accurately predicting future sales trends, thereby enabling retail businesses to make well-informed decisions. The paper highlights the critical role of accurate sales forecasting in driving strategic decision-making processes within the retail sector. In an increasingly dynamic and competitive marketplace, precise sales predictions serve as indispensable tools for guiding business strategies, optimizing inventory management, and enhancing overall operational efficiency. Through the adept application of advanced analytical techniques and a comprehensive understanding of seasonal sales dynamics, this study offers valuable insights into sales prediction methodologies. By equipping retail enterprises with the means to anticipate market fluctuations and consumer trends, the research empowers businesses to navigate the ever-evolving retail landscape with confidence and strategic foresight.
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Keywords: Time series forecasting, Machine learning, Sales prediction, ARIMA, ADF, Seasonal sales, Retail businesses
n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Algorithms Design and Analysis Review(ijadar)]
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References
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[1] Malik N, Singh K. Sales prediction model for Big Mart. Parichay: Maharaja Surajmal Institute Journal of Applied Research. 2020;3(1):22-32.
[2] Zhang Z. Sales Prediction Based on ARIMA Time Series and Multifactorial Linear Model. Highlights in Science, Engineering and Technology. 2023 Mar 16;38:1-8.
[3] Shakti SP, Hassan MK, Zhenning Y, Caytiles RD, Iyenger NC. Annual automobile sales prediction using ARIMA model. International Journal of Hybrid Information Technology. 2017 Jun;10(6):13-22.
[4] Permatasari CI, Sutopo W, Hisjam M. Sales forecasting newspaper with ARIMA: A case study. InAIP Conference Proceedings 2018 Feb 9 (Vol. 1931, No. 1). AIP Publishing.
[5] Han Y. A forecasting method of pharmaceutical sales based on ARIMA-LSTM model. In2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT) 2020 Nov 13 (pp. 336-339). IEEE.
[6] Zhang M. Financial Time Series Frequent Pattern Mining Algorithm Based on Time Series ARIMA Model. In2023 International Conference on Networking, Informatics and Computing (ICNETIC) 2023 May 29 (pp. 244-247). IEEE.
[7] Wang Z, Lou Y. Hydrological time series forecast model based on wavelet de-noising and ARIMA-LSTM. In2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) 2019 Mar 15 (pp. 1697-1701). IEEE.
[8] Wang Q. Time series simulation method of meteorological elements based on ARIMA model. In2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE) 2021 Sep 24 (pp. 358-362). IEEE.
[9] Han J, Fu Y, Zhang H. Optimization Research on Vegetable Sales in Supermarkets Based on Greedy Algorithm and ARIMA Time Series Model. In2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) 2024 Feb 27 (pp. 979-985). IEEE.
[10] Kogekar AP, Nayak R, Pati UC. Forecasting of water quality for the river ganga using univariate time-series models. In2021 8th International Conference on Smart Computing and Communications (ICSCC) 2021 Jul 1 (pp. 52-57). IEEE.
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International Journal of Algorithms Design and Analysis Review
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| Volume | 02 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | April 18, 2024 | |
| Accepted | April 20, 2024 | |
| Published | May 30, 2024 |
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