Analyze sales forecast accuracy

You are tasked with analyzing sales forecast accuracy. You will be provided with actual sales data and forecast data. Your goal is to compare the two, calculate accuracy metrics, and provide insights on the forecast performance. First, you will receive the actual sales data: <sales_data> {{SALES_DATA}} </sales_data> Next, you will receive the forecast data: <forecast_data> {{FORECAST_DATA}} </forecast_data> To analyze the forecast accuracy, follow these steps: 1. Data Preparation: - Ensure that the sales data and forecast data are for the same time periods and products/categories. - If there are any mismatches or missing data, note them in your analysis. 2. Calculate Accuracy Metrics: - Mean Absolute Percentage Error (MAPE): Calculate the average percentage difference between forecast and actual sales. - Mean Absolute Error (MAE): Calculate the average absolute difference between forecast and actual sales. - Bias: Determine if the forecast consistently over- or under-predicts sales. 3. Analyze Trends and Patterns: - Identify any seasonal patterns or trends in forecast accuracy. - Note any specific time periods or products/categories where the forecast was particularly accurate or inaccurate. 4. Provide Insights: - Explain possible reasons for discrepancies between forecast and actual sales. - Suggest potential improvements to the forecasting process based on your findings. Present your analysis in the following format: <analysis> <summary> Provide a brief overview of the forecast accuracy, including the overall MAPE and any significant findings. </summary> <detailed_metrics> List the calculated accuracy metrics (MAPE, MAE, Bias) for the entire dataset and any relevant subsets (e.g., by product category or time period). </detailed_metrics> <trends_and_patterns> Describe any observed trends or patterns in forecast accuracy, including seasonal variations or product-specific insights. </trends_and_patterns> <insights_and_recommendations> Offer insights into the reasons for forecast discrepancies and provide recommendations for improving forecast accuracy. </insights_and_recommendations> </analysis> Remember to support your analysis with specific data points and calculations. If you need to make any assumptions or interpretations, clearly state them in your analysis.