geoskill-ai-time-series-forecast

LSTM 时序预测网络（torch+CUDA，默认） + 经典线性/多项式/AR(p) 解释基线：多步外推 + 留出 MAE/RMSE 验证 + 逐像元栅格输出

AI时序预测 | AI Time Series Forecast

Performs multi-step extrapolation of remote sensing time series (per-pixel NDVI/temperature/backscatter) with a single-layer LSTM, validates on a held-out period (MAE/RMSE), and outputs forecast rasters for each future step plus a per-pixel validation RMSE map.

Core model: single-layer LSTM (many-to-one + recursive extrapolation), trained/inferred by default on torch + CUDA; the skill ships with pretrained weights ts_lstm_weights.pt (automatically trained on GPU and persisted at first run when missing). Three interpretable classical baselines (--method linear|poly|ar) are also retained for comparison and GPU-free environments. The fitting accuracy of each model class, AR coefficient recovery, and held-out MAE/RMSE all have unit tests against hand-computed baselines.

Dependencies / 依赖

bash

pip install numpy rasterio scipy torch --index-url https://download.pytorch.org/whl/cu121

Classical baseline methods (--method linear|poly|ar) do not require torch.

Usage / 使用方法

Example 1 (Synthetic Data, Offline)

bash

python geoskill-ai-time-series-forecast.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

Example 2: Synthetic Time-Series Forecast (Offline)

bash

python geoskill-ai-time-series-forecast.py --bbox 116.0 39.0 117.0 40.0 --synthetic --method linear --horizon 4 --output-dir ./out

Example 3: Polynomial Trend Extrapolation

bash

python geoskill-ai-time-series-forecast.py --bbox 116.0 39.0 117.0 40.0 --synthetic --method poly --degree 2 --horizon 6 --output-dir ./out

Example 4: Autoregressive Model

bash

python geoskill-ai-time-series-forecast.py --input series.tif --method ar --order 3 --horizon 3 --output-dir ./out

Output / 输出

File	Format	Description
forecast.tif	GeoTIFF	Forecasts for the next horizon steps (one band per step)
validation_rmse.tif	GeoTIFF	Per-pixel held-out validation RMSE
forecast_report.json	JSON	Global/center-pixel forecasts and error metrics
output-manifest.json	JSON	Run manifest (inputs/outputs/QA/exit code)

Data Source / 数据源 / Source

Local multi-band GeoTIFF (bands = time steps), or --synthetic (NDVI-like cube of trend + annual cycle + noise).

Privacy / 隐私声明 / Privacy

• Runs offline by default; --synthetic mode requires no network at all.
• All processing is done locally; no user data is ever uploaded.

License / License

MIT

---

---

name: geoskill-ai-time-series-forecast description: 'LSTM 时序预测网络（torch+CUDA，默认） + 经典线性/多项式/AR(p) 解释基线：多步外推 + 留出 MAE/RMSE 验证 + 逐像元栅格输出'

AI时序预测 | AI Time Series Forecast

对遥感时序（逐像元 NDVI/温度/后向散射）做单层 LSTM 多步外推，并在留出时段上验证（MAE/RMSE），输出未来各步预测栅格与逐像元验证 RMSE 图。

核心模型：单层 LSTM（many-to-one + 递归外推），默认在 torch + CUDA 上训练/推理；随 skill 附带预训练权重 ts_lstm_weights.pt（缺失时在首次运行时在 GPU 上自动训练并落盘）。同时保留三套可解释经典基线（--method linear|poly|ar）用于对比与无 GPU 环境。每一类模型的拟合精度、AR 系数恢复、留出 MAE/RMSE 均有手算基准的单元测试。

依赖

bash

pip install numpy rasterio scipy torch --index-url https://download.pytorch.org/whl/cu121

如要跑经典基线方法（--method linear|poly|ar）无需 torch。

使用方法

示例 1（合成数据，离线）

bash

python geoskill-ai-time-series-forecast.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out

示例 2：合成时序预测（离线）

bash

python geoskill-ai-time-series-forecast.py --bbox 116.0 39.0 117.0 40.0 --synthetic --method linear --horizon 4 --output-dir ./out

示例 3：多项式趋势外推

bash

python geoskill-ai-time-series-forecast.py --bbox 116.0 39.0 117.0 40.0 --synthetic --method poly --degree 2 --horizon 6 --output-dir ./out

示例 4：自回归模型

bash

python geoskill-ai-time-series-forecast.py --input series.tif --method ar --order 3 --horizon 3 --output-dir ./out

输出

文件	格式	说明
forecast.tif	GeoTIFF	未来 horizon 步预测（每步一个波段）
validation_rmse.tif	GeoTIFF	逐像元留出验证 RMSE
forecast_report.json	JSON	全局/中心像元预测与误差指标
output-manifest.json	JSON	运行清单（输入/输出/QA/退出码）

数据源 / Source

本地多波段 GeoTIFF（波段 = 时间步），或 --synthetic（趋势 + 年周期 + 噪声的 NDVI 式立方体）。

隐私声明 / Privacy

• 默认离线运行，--synthetic 模式完全无网络。
• 所有处理在本地完成，不上传任何用户数据。

License

MIT