How seangvutha8-web/time-series-indian-temperature- works
This is a Streamlit dashboard for exploring India’s monthly temperature history and producing forecasts. The user workflow spans dataset loading, exploratory time-series analysis, model forecasts and diagnostics, model comparison, and future projections. The application is implemented in one file, so its domain components share the same source path. The excerpts show the Kaggle download and CSV read, but omit most page-routing and model code; uncertain connections are therefore not asserted.
Main entry points: app.py Streamlit dashboard
Components 11
Dashboard
- Streamlit dashboard
app.py - Dataset loading
app.py - Temperature CSV
Time-Series Analysis
- Trend and decomposition
app.py - ACF and PACF analysis
app.py
Forecasting
- Model forecasts
app.py - Residual diagnostics
app.py - Forecast comparison
app.py - Future projections
app.py
Other components
- Dashboard user
- Kaggle dataset service
Connections 10
- Dashboard user → Streamlit dashboard: uses
app.py - Streamlit dashboard → Dataset loading: loads data
app.py - Dataset loading → Kaggle dataset service: downloads dataset
app.py - Dataset loading → Temperature CSV: reads CSV
app.py - Streamlit dashboard → Trend and decomposition: presents analysis
app.py - Streamlit dashboard → ACF and PACF analysis: presents analysis
app.py - Streamlit dashboard → Model forecasts: presents forecasts
app.py - Streamlit dashboard → Residual diagnostics: presents diagnostics
app.py - Streamlit dashboard → Forecast comparison: presents comparison
app.py - Streamlit dashboard → Future projections: presents projections
app.py