PRO Dashboard: ARIMA/SARIMA Time Series¶
Interactive dashboard with a professional design inspired by Bloomberg-style financial terminals, for complete time series analysis following the Box-Jenkins methodology.
Dashboard Features¶
| Element | Description |
|---|---|
| Theme | Dark financial terminal style (inspired by Bloomberg/OECD Explorer) |
| KPIs | Cards with real-time metrics: RMSE, MAE, MAPE, R2, AIC |
| Tabs | 7 interactive sections with smooth transitions |
| Responsive | Adaptable to different screen sizes |
Content by Tab¶
| # | Tab | Description |
|---|---|---|
| 1 | Original Series | Airline passengers 1949-1960 with 12M moving average |
| 2 | Decomposition | Trend + Seasonality + Residual (multiplicative) |
| 3 | ACF / PACF | Autocorrelation of the differenced series (d=1, D=1, s=12) |
| 4 | Diagnostics | Residuals, histogram, residual ACF, Q-Q plot |
| 5 | Forecast | Series + SARIMA fit + 24-month forecast + 95% CI |
| 6 | Radar Metrics | Polar visualization of normalized metrics |
| 7 | Comparison | Comparison of different SARIMA orders |
Design Inspiration¶
This dashboard was created following best practices in financial visualization:
- OECD Pension Explorer - Official OECD Plotly App
- Portfolio Optimizer - Panel/HoloViz Gallery
- Dash Bootstrap Templates - Professional themes for Dash
Box-Jenkins Methodology¶
- Identification: ACF/PACF analysis to determine orders (p, d, q)(P, D, Q)[s]
- Estimation: Maximum likelihood fitting with SARIMAX
- Diagnostics: Ljung-Box, Jarque-Bera tests, Q-Q plot
- Forecasting: Forecast with 95% confidence intervals
Source Code¶
- Exercise script:
ejercicios/04_machine_learning/07_series_temporales_arima/ - Dashboard exporter:
.profesor/soluciones/TRABAJO_FINAL/export_arima_pro.py - Theoretical guide: ARIMA Time Series
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Course: Big Data with Python - From Zero to Production Professor: Juan Marcelo Gutierrez Miranda | @TodoEconometria Hash ID: 4e8d9b1a5f6e7c3d2b1a0f9e8d7c6b5a4f3e2d1c0b9a8f7e6d5c4b3a2f1e0d9c
Academic references:
- Box, G.E.P. & Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control. Holden-Day.
- Hyndman, R.J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.
- Hamilton, J.D. (1994). Time Series Analysis. Princeton University Press.