Time Series Data Analyzer
A time series analysis tool with trend detection, seasonality decomposition, forecasting, and anomaly detection. Supports various data sources, multiple forecasting algorithms, and interactive char...
Target user
Data scientists and analysts who work with time series data
Problem
Analysts need to analyze time series data for trends, seasonality, and anomalies with forecasting without setting up complex statistical environments for each analysis.
Requirements
- Load time series data from multiple sources
- Detect trends and seasonal patterns
- Decompose into trend, seasonal, residual
- Forecast with confidence intervals
- Detect anomalies in the series
Core technologies
Python, statsmodels, FastAPI
Completion criteria
Users can analyze time series for trends, seasonality, forecast future values, and detect anomalies.
Optional extensions
- Add multiple forecasting models
- Implement cross-validation
- Add interactive parameter tuning
- Support multivariate series
Project phases
Phase 1: Phase 1: Data Loading
Tasks: Set up Python project, Build data source connectors, Implement time series parsing, Add data validation
Phase 2: Phase 2: Analysis
Tasks: Implement trend detection, Add seasonal decomposition, Build anomaly detection, Create statistical summaries
Phase 3: Phase 3: Forecasting and Visualization
Tasks: Implement forecasting algorithms, Add confidence intervals, Create interactive charts, Build comparison view