kyLELEng/weather-scenario-diffusion-1d

WeatherScenarioDiffusion-1D

WeatherScenarioDiffusion-1D is a conditional 1D diffusion model for multivariate weather time-series scenario generation.

The model is trained on Duyu/Time-Series-Forecasting-Benchmark-Datasets, file Weather.csv.

What The Model Does

This is a single conditional diffusion model with three usage modes:

1. Unconditional scenario generation: sample realistic multivariate weather trajectories from noise.
2. Future-mask generation: condition on the first part of a window and generate the missing future segment.
3. Channel inpainting: condition on known weather variables and generate missing variables.

The model uses:

• diffusers.UNet1DModel
• diffusers.DDPMScheduler
• mask conditioning through concatenated input channels: noisy_x, observed_x, and observed_mask

Model Size

• Parameters: 41,556,501
• Weight dtype: float32
• Weight file: diffusion_pytorch_model.safetensors
• Weight file size: approximately 166 MB

Data

• Source dataset: Duyu/Time-Series-Forecasting-Benchmark-Datasets
• Source file: Weather.csv
• Numeric channels detected: 21
• Window length: 256
• Stride: 4
• Split: time-ordered 80% train / 10% validation / 10% test
• Normalization: z-score fitted only on the train split

Detected channels:

[
  "feature_00",
  "feature_01",
  "feature_02",
  "feature_03",
  "feature_04",
  "feature_05",
  "feature_06",
  "feature_07",
  "feature_08",
  "feature_09",
  "feature_10",
  "feature_11",
  "feature_12",
  "feature_13",
  "feature_14",
  "feature_15",
  "feature_16",
  "feature_17",
  "feature_18",
  "feature_19",
  "feature_20"
]

Training

{
  "dataset_repo": "Duyu/Time-Series-Forecasting-Benchmark-Datasets",
  "dataset_file": "Weather.csv",
  "model_repo_id": "kyLELEng/weather-scenario-diffusion-1d",
  "output_dir": "/tmp/weather-scenario-diffusion-1d",
  "window_length": 256,
  "stride": 4,
  "max_train_steps": 8000,
  "train_batch_size": 128,
  "eval_batch_size": 128,
  "num_workers": 8,
  "learning_rate": 0.0002,
  "weight_decay": 0.01,
  "grad_clip_norm": 1.0,
  "num_train_timesteps": 1000,
  "eval_every": 1000,
  "save_every": 2000,
  "num_eval_batches": 12,
  "sample_inference_steps": 80,
  "sample_count": 24,
  "mixed_precision": "bf16",
  "seed": 42,
  "model_size": "large",
  "smoke_test": false
}

The training objective is noise prediction:

MSE(predicted_noise, true_noise)

Known observed regions are provided as conditioning input. The loss is weighted toward unknown/masked regions so the model learns conditional reconstruction as well as unconditional generation.

Evaluation

{
  "future_mask_mse_zspace": 0.16154611110687256,
  "channel_inpainting_mse_zspace": 0.10761465132236481,
  "generated_real_correlation_mae": 0.3473077408348441,
  "abs_autocorrelation_mae": 0.6126329355779511,
  "real_distribution": {
    "mean": [
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    "std": [
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    "q50": [
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    "q95": [
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      -0.06447356939315796,
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  },
  "generated_distribution": {
    "mean": [
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      0.2204890251159668,
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      0.23873503506183624,
      0.01685507781803608,
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      0.028043851256370544,
      0.12692318856716156,
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    ],
    "std": [
      0.2767521142959595,
      0.3012436330318451,
      0.2993737757205963,
      0.30074891448020935,
      0.3890363872051239,
      0.3219568431377411,
      0.3009476661682129,
      0.37483009696006775,
      0.30024605989456177,
      0.304373562335968,
      0.2866939902305603,
      0.19090980291366577,
      0.41177040338516235,
      0.398755818605423,
      0.19313617050647736,
      0.23614700138568878,
      0.4057450592517853,
      0.40770408511161804,
      0.407763808965683,
      0.31721681356430054,
      0.20663630962371826
    ],
    "q05": [
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      -0.2666028141975403,
      -0.2890886068344116,
      -0.25566428899765015,
      -0.6129659414291382,
      -0.38257062435150146,
      -0.19731372594833374,
      -0.5293506979942322,
      -0.24754241108894348,
      -0.2712758183479309,
      -0.6728720664978027,
      -0.2995768189430237,
      -0.6813194751739502,
      -0.5154849290847778,
      -0.36515557765960693,
      -0.429704487323761,
      -0.5263148546218872,
      -0.5553821921348572,
      -0.49782344698905945,
      -0.2676949203014374,
      -0.3324751853942871
    ],
    "q50": [
      0.10788173228502274,
      0.21172723174095154,
      0.18094468116760254,
      0.2328486293554306,
      0.0098641999065876,
      0.10586627572774887,
      0.28131914138793945,
      0.0327904112637043,
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      -0.18645159900188446,
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      -0.036815427243709564,
      0.09912104904651642,
      0.23998694121837616,
      0.019317764788866043
    ],
    "q95": [
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      0.7389823198318481,
      0.7046443223953247,
      0.7413685321807861,
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  },
  "real_abs_autocorr_lag1": [
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    0.9903800119819881,
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    0.9904251617511753,
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    NaN,
    NaN,
    0.9654974645960943,
    0.97213405366258,
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    0.9674985063407219
  ],
  "generated_abs_autocorr_lag1": [
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    0.008822063729828335
  ],
  "training_history": [
    {
      "step": 1000,
      "train_loss": 0.07410214841365814,
      "validation_denoising_loss": 0.05256535982092222
    },
    {
      "step": 2000,
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    {
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    {
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    {
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    {
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    {
      "step": 7000,
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    },
    {
      "step": 8000,
      "train_loss": 0.018115710467100143,
      "validation_denoising_loss": 0.11552448819080989
    }
  ],
  "best_validation_denoising_loss": 0.05256535982092222,
  "final_step": 8000
}

Evaluation is based on held-out windows and includes:

• validation denoising loss
• future-mask inpainting MSE
• channel-inpainting MSE
• generated-vs-real distribution statistics
• cross-channel correlation matrix error
• absolute-value autocorrelation error

Intended Use

This model is for research and demonstration of multivariate time-series diffusion. It is not a production forecasting system.

Files

• config.json: 1D U-Net model configuration
• diffusion_pytorch_model.safetensors: model weights
• scheduler/scheduler_config.json: DDPM scheduler configuration
• preprocess_config.json: dataset, split, normalization, window, and channel metadata
• normalization_stats.json: train-split mean and standard deviation
• evaluation_report.json: held-out evaluation metrics
• sample_plots/: generated examples and conditional samples