Sales Forecasting

Overview of the Project:

This project involves predicting store sales over time using statistical modeling techniques, specifically ARIMA (AutoRegressive Integrated Moving Average) and SARIMAX (Seasonal AutoRegressive Integrated Moving-Average with eXogenous factors) models. The goal is to accurately forecast future sales trends based on historical sales data, helping stakeholders in decision-making processes such as inventory management, promotional planning, and revenue forecasting.
The dataset was loaded using pandas, followed by an exploration of the top rows to understand its structure.
The initial steps include handling missing values, outliers, and ensuring the data is in the appropriate time-series format.

Exploratory Data Analysis (EDA):

Visualizations of the sales trends over time, detecting seasonality, trends, and noise in the dataset.
Modeling Approach:

ARIMA Modeling: The model captures the autoregressive and moving average components of the time series. It is tuned by specifying the order parameters (p, d, q).
SARIMAX Modeling: An advanced version of ARIMA, this model captures seasonal patterns and can incorporate external factors (exogenous variables).

Evaluation Metrics: The performance of the models was likely evaluated using metrics such as RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and visual inspections of the predicted versus actual sales.

Evaluation:
Insights Gained: The models provide actionable insights into future sales trends. Understanding the sales pattern helps in making informed decisions about resource allocation and marketing.
The project concludes with a comparison between the ARIMA and SARIMA models.
The SARIMA model provides better results, as seen in the sales forecasting plot that compares the predicted values from both models against the actual sales.
The SARIMA model (green line) closely follows the actual sales trend, especially capturing the seasonal peaks, whereas the ARIMA model (red line) has more deviations from the actual data.

Conclusion:
SARIMA outperforms ARIMA in terms of sales prediction accuracy.
The inclusion of seasonality in SARIMA makes it better suited for capturing the underlying patterns in store sales data, which often have recurring seasonal peaks and trends.
The figure clearly shows that SARIMA is the preferred model for this particular time series data due to its capability to handle seasonality. ​​