Kanishk Patil
Anuj Sawant
Shantanu Rane
Siddhant Pupulwad
DK Chitre
- Student Department of computer engineering, Terna Engineering College, Navi Mumbai Maharashtra India
- Student Department of computer engineering, Terna Engineering College, Navi Mumbai Maharashtra India
- Student Department of computer engineering, Terna Engineering College, Navi Mumbai Maharashtra India
- Student Department of computer engineering, Terna Engineering College, Navi Mumbai Maharashtra India
- Assistant Professor Department of computer engineering, Terna Engineering College, Navi Mumbai Maharashtra India
Abstract
Sales 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.
Keywords: Time series forecasting, Machine learning, Sales prediction, ARIMA, ADF, Seasonal sales, Retail businesses
[This article belongs to International Journal of Algorithms Design and Analysis Review(ijadar)]
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International Journal of Algorithms Design and Analysis Review
| Volume | 02 |
| Issue | 01 |
| Received | April 18, 2024 |
| Accepted | April 20, 2024 |
| Published | May 30, 2024 |
