Time Series Modeling for Retail Sales Forecasting

[sharayuanuse]

Is your feature request related to a problem? Please describe.

Currently, there is a need for a robust analysis and forecasting model to predict retail sales in the USA. The existing methods may not capture seasonal trends and fluctuations accurately, leading to suboptimal forecasting performance.

Describe the solution you'd like

Implement Time Series Modeling techniques to analyze and forecast retail sales data from 1992 to 2017. This should involve applying KPSS and Dickey-Fuller tests to ensure stationarity, utilizing ACF and PACF plots for parameter estimation, and exploring various models from the fpp2 forecasting package in R, including Seasonal Naive, Exponential Smoothing, and ARIMA models.

Describe alternatives you've considered

Consideration of simpler forecasting methods, such as linear regression models or moving averages, but these may not adequately address the seasonality and trends present in the retail sales data.

Approach to be followed (optional)

• Data Preparation: Clean and preprocess the monthly retail sales data from 1992 to 2017.
• Stationarity Testing: Apply KPSS and Dickey-Fuller tests to confirm stationarity of the series.
• Parameter Estimation: Utilize ACF and PACF plots to determine the appropriate parameters for the models.
• Model Selection: Explore different forecasting models from the fpp2 package, including Seasonal Naive, Exponential Smoothing, and ARIMA.
• Model Evaluation: Calculate MAPE and other performance metrics to evaluate the models, aiming for a MAPE of less than 2%.
• Forecasting: Use the selected model to forecast retail sales for the next two years.

Additional context

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