Time-Series Anomaly Detection for Retail Peak Sales Forecasting

The project aims to forecast anomalous peaks and troughs in retail sales, enabling retailers to proactively manage inventory, staffing, and marketing efforts. The core idea is inspired by the cyclical and predictive nature of catastrophic events in 'Nightfall' and '12 Monkeys'. Just as characters in those stories try to understand and predict these events, this project aims to understand and predict anomalous sales patterns.

The project works as follows:

1. Data Acquisition: The 'Retail Sales' scraper (existing/readily available online) gathers historical sales data from various sources like online retail APIs, publicly available datasets, or even individual retailer websites (with appropriate permissions). Data should include timestamps, sales figures, product categories, location (if applicable), and any available promotional information. Consider adding external data sources like weather information, social media sentiment, and economic indicators.
2. Data Preprocessing: Clean and prepare the scraped data. This involves handling missing values, outlier removal, data normalization, and feature engineering (e.g., creating lagged variables, calculating moving averages, identifying seasonality).
3. Time-Series Analysis: Apply time-series analysis techniques to the data. ARIMA, Prophet, or other suitable models can be used to forecast future sales based on historical trends. The '12 Monkeys' film uses a time-travel approach to understanding events, this project will use a temporal dataset to uncover relationships in retail sales.
4. Anomaly Detection: Implement anomaly detection algorithms to identify deviations from the expected sales patterns. Techniques like Isolation Forests, One-Class SVM, or even simpler methods based on standard deviations from the moving average can be employed. The 'Nightfall' analogy is about predicting when the societal collapse occurs due to a rare eclipse, here we are predicting when the rare sales event will occur.
5. External Factors Integration: Integrate external factors (weather, social media sentiment, economic indicators) as additional features to improve the accuracy of both the time-series forecasting and anomaly detection.
6. Alerting System: Build a system that generates alerts when an anomaly is detected. This could involve sending email notifications, updating a dashboard, or triggering automated adjustments to inventory or staffing.
7. Backtesting and Evaluation: Rigorously backtest the model using historical data to evaluate its performance. Metrics like precision, recall, F1-score, and Mean Absolute Percentage Error (MAPE) can be used to assess the accuracy of the anomaly detection and forecasting.

Niche and Low-Cost: This project targets a specific problem (anomaly detection in retail sales) making it niche. The data can be obtained through scraping or publicly available datasets, keeping costs low. Implementation utilizes readily available Python libraries like Pandas, NumPy, Scikit-learn, and Prophet.

High Earning Potential: By providing early warnings of sales surges or collapses, retailers can optimize their operations, leading to significant cost savings and increased revenue. The project can be monetized by:

- Consulting: Offering consulting services to retailers to implement and customize the anomaly detection system.
- Software-as-a-Service (SaaS): Developing a subscription-based SaaS platform that provides anomaly detection and sales forecasting capabilities.
- Data Products: Selling aggregated and anonymized anomaly insights to market research firms or financial institutions.

The project's focus on predictive analytics and its clear path to monetization makes it a good candidate for individual implementation with high earning potential.