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
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)
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)
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
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
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;
--syntheticmode 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
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）
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）
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
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
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