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| 1 | +--- |
| 2 | +title: "Deep dive into time series analysis with GRASS" |
| 3 | +author: "Veronica Andreo" |
| 4 | +date: 2025-05-27 |
| 5 | +date-modified: today |
| 6 | +image: images/tgrass_flowchart.png |
| 7 | +format: |
| 8 | + ipynb: default |
| 9 | + html: |
| 10 | + toc: true |
| 11 | + code-tools: true |
| 12 | + code-copy: true |
| 13 | + code-fold: false |
| 14 | +categories: [time series, raster, advanced, Python] |
| 15 | +description: "A collection of tutorials demonstrating how to handle time series data in GRASS, from basic concepts to advanced spatiotemporal analysis." |
| 16 | +engine: jupyter |
| 17 | +execute: |
| 18 | + eval: false |
| 19 | +--- |
| 20 | + |
| 21 | +**GRASS** offers robust tools for working with spatiotemporal data, especially raster time series. This page collects all the tutorials that focus on time series workflows, from creating space-time datasets to performing time-aware analysis and visualizations. |
| 22 | + |
| 23 | +Whether you're just getting started or looking to perform advanced temporal algebra, you'll find a range of examples below. |
| 24 | + |
| 25 | +## 📚 Tutorial Collection |
| 26 | + |
| 27 | +Here are the available tutorials, ordered to guide you from basic to more advanced concepts: |
| 28 | + |
| 29 | +1. **[Introduction to Time Series in GRASS](./time_series_management_and_visualization.qmd)** |
| 30 | + Learn the basics of space-time datasets and time series visualization. |
| 31 | + |
| 32 | +2. **[Temporal aggregations](./time_series_aggregations.qmd)** |
| 33 | + Group and summarize time series data by week, month, or season. |
| 34 | + |
| 35 | +3. **[Temporal algebra](./time_series_algebra.qmd)** |
| 36 | + Use temporal algebra to query and analyze space-time datasets based on time relations. |
| 37 | + |
| 38 | +4. **[Temporal accumulation](./time_series_accumulations.qmd)** |
| 39 | + Compute cumulative temperature values over time and find suitable areas for mosquitoes. |
| 40 | + |
| 41 | +5. **[Temporal gap-filling](./time_series_gap_filling.qmd)** |
| 42 | + Fill missing values using temporal interpolation and smoothing techniques. |
| 43 | + |
| 44 | +6. **[Temporal query with vector data](./time_series_query_with_vector.qmd_)** |
| 45 | + Extract time series values at specific vector locations (e.g., points or polygons). |
| 46 | + |
| 47 | +7. **[Temporal subset, import and export](./time_series_extraction.qmd)** |
| 48 | + Subset time series by date, and learn how to import/export space-time datasets effectively. |
| 49 | + |
| 50 | +## 🧭 What's Next? |
| 51 | + |
| 52 | +You can also explore related categories: |
| 53 | + |
| 54 | +- [Raster analysis](https://grass-tutorials.osgeo.org/#category=raster) |
| 55 | +- [Python scripting](https://grass-tutorials.osgeo.org/#category=Python) |
| 56 | + |
| 57 | +Or [return to the home page](https://grass-tutorials.osgeo.org/) to browse all tutorials. |
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