AI Analysis
Final verdict: SUSPICIOUS
The package exhibits signs of obfuscation and has incomplete metadata, raising concerns about its integrity and potential for hidden functionality.
- High obfuscation risk due to the use of __import__ with fromlist.
- Incomplete metadata including missing author information and an external non-HTTPS link.
Per-check LLM notes
- Network: No network calls detected, which is normal for a package focused on time series forecasting without real-time data requirements.
- Shell: No shell execution patterns detected, aligning with expectations for a benign Python package.
- Obfuscation: The use of __import__ with fromlist suggests an attempt to hide the direct import statement, which is unusual and could indicate obfuscation.
- Credentials: No suspicious patterns related to credential harvesting were found.
- Metadata: The package shows some red flags such as a missing author name and an external non-HTTPS link, but no clear signs of typosquatting or other malicious intent.
Package Quality Overall: Medium (5.4/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/weibinxu86/autotsforecast#readmeDetailed PyPI description (24345 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
230 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
5.0
Limited contributor diversity
1 unique contributor(s) across 47 commits in weibinxu86/autotsforecastSingle author but highly active (47 commits)
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 10.0
Found 6 obfuscation pattern(s)
h self.model.eval() # Scale last values last_scaled =("ThetaForecaster", lambda: __import__( "autotsforecast.models.external", fromlist=["ThetaForecaster"] ).ThetaForecaster(horizon=horizon)), ("XGBoostFor("XGBoostForecaster", lambda: __import__( "autotsforecast.models.external", fromlist=["XGBoostForecaster"] ).XGBoostForecaster(horizon=horizon, n_lags=14, n_estimators="LightGBMForecaster", lambda: __import__( "autotsforecast.models.external", fromlist=["LightGBMForecaster"] ).LightGBMForecaster(horizon=horizon, n_lags=14, n_estimators"CatBoostForecaster", lambda: __import__( "autotsforecast.models.external", fromlist=["CatBoostForecaster"] ).CatBoostForecaster(horizon=horizon, n_lags=14, n_estimators("ProphetForecaster", lambda: __import__( "autotsforecast.models.external", fromlist=["ProphetForecaster"] ).ProphetForecaster(horizon=horizon)), ("NBEATSFo
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: gmail.com>
Suspicious Page Links
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://0.0.0.0:8000
Git Repository History
Repository weibinxu86/autotsforecast appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with autotsforecast
Create a web-based time series forecasting application using the 'autotsforecast' package. This application should allow users to upload their own time series datasets, select from a variety of forecasting models available within 'autotsforecast', and receive predictions along with visualizations of the forecasted data. Additionally, implement anomaly detection to highlight unusual patterns in the data. The application should have the following features: 1. **User Data Upload**: Users should be able to upload CSV files containing time series data. 2. **Model Selection**: Provide a dropdown menu allowing users to choose from at least five different forecasting models included in 'autotsforecast'. 3. **Forecast Generation**: Once a model is selected, generate a forecast for the next 10 periods and display it graphically. 4. **Anomaly Detection**: Implement anomaly detection on the uploaded data and highlight any detected anomalies in the visualization. 5. **Data Profiling**: Automatically profile the uploaded dataset to provide insights into its characteristics such as mean, variance, trend, seasonality, etc. 6. **FastAPI Integration**: Use FastAPI to serve the application and enable API endpoints for programmatic access. 7. **LangChain Support**: Allow users to chain multiple forecasting models together for ensemble predictions. 8. **Documentation and User Guide**: Include comprehensive documentation explaining how to use the application and what each feature does. The application should utilize 'autotsforecast' for all forecasting tasks, leveraging its built-in models, parallel model search capabilities, and anomaly detection functionalities. Ensure the application is user-friendly and visually appealing.
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