# seangvutha8-web/time-series-indian-temperature- architecture

> Architecture diagram of the GitHub repository seangvutha8-web/time-series-indian-temperature-, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-11-10). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/seangvutha8-web/time-series-indian-temperature-
- Repository: https://github.com/seangvutha8-web/time-series-indian-temperature-

## Overview

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`](https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py) (Streamlit dashboard)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_entry["Dashboard"]
  node_dashboard["Streamlit dashboard<br/>[app.py]"]
  node_data["Dataset loading<br/>[app.py]"]
  node_temperature_data["Temperature CSV"]
end

subgraph group_analysis["Time-Series Analysis"]
  node_exploration["Trend and decomposition<br/>[app.py]"]
  node_correlation["ACF and PACF analysis<br/>[app.py]"]
end

subgraph group_forecasting["Forecasting"]
  node_forecast["Model forecasts<br/>[app.py]"]
  node_residuals["Residual diagnostics<br/>[app.py]"]
  node_comparison["Forecast comparison<br/>[app.py]"]
  node_future["Future projections<br/>[app.py]"]
end

node_user(("Dashboard user"))
node_kaggle[("Kaggle dataset service")]

node_user -->|"uses"| node_dashboard
node_dashboard -.->|"loads data"| node_data
node_data -->|"downloads dataset"| node_kaggle
node_data -->|"reads CSV"| node_temperature_data
node_dashboard -.->|"presents analysis"| node_exploration
node_dashboard -.->|"presents analysis"| node_correlation
node_dashboard -.->|"presents forecasts"| node_forecast
node_dashboard -.->|"presents diagnostics"| node_residuals
node_dashboard -.->|"presents comparison"| node_comparison
node_dashboard -.->|"presents projections"| node_future

click node_dashboard "https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py"
click node_data "https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py"
click node_exploration "https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py"
click node_correlation "https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py"
click node_forecast "https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py"
click node_residuals "https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py"
click node_comparison "https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py"
click node_future "https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_dashboard,node_data,node_temperature_data,node_user toneBlue
class node_exploration,node_correlation,node_kaggle toneAmber
class node_forecast,node_residuals,node_comparison,node_future toneMint
```

## Components

### Dashboard

- [Streamlit dashboard](https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py): `app.py`
- [Dataset loading](https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py): `app.py`
- **Temperature CSV**

### Time-Series Analysis

- [Trend and decomposition](https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py): `app.py`
- [ACF and PACF analysis](https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py): `app.py`

### Forecasting

- [Model forecasts](https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py): `app.py`
- [Residual diagnostics](https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py): `app.py`
- [Forecast comparison](https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py): `app.py`
- [Future projections](https://github.com/seangvutha8-web/time-series-indian-temperature-/blob/main/app.py): `app.py`

### Other components

- **Dashboard user**
- **Kaggle dataset service**

## Connections

- Dashboard user → Streamlit dashboard: uses (evidence: `app.py`)
- Streamlit dashboard → Dataset loading: loads data (evidence: `app.py`)
- Dataset loading → Kaggle dataset service: downloads dataset (evidence: `app.py`)
- Dataset loading → Temperature CSV: reads CSV (evidence: `app.py`)
- Streamlit dashboard → Trend and decomposition: presents analysis (evidence: `app.py`)
- Streamlit dashboard → ACF and PACF analysis: presents analysis (evidence: `app.py`)
- Streamlit dashboard → Model forecasts: presents forecasts (evidence: `app.py`)
- Streamlit dashboard → Residual diagnostics: presents diagnostics (evidence: `app.py`)
- Streamlit dashboard → Forecast comparison: presents comparison (evidence: `app.py`)
- Streamlit dashboard → Future projections: presents projections (evidence: `app.py`)