data:
  type: 'PEMSD3'
  num_nodes: 358
  lag: 12
  horizon: 12
  val_ratio: 0.2
  test_ratio: 0.2
  tod: false
  normalizer: std
  column_wise: false
  default_graph: true
  add_time_in_day: true
  add_day_in_week: true
  steps_per_day: 288
  days_per_week: 7
  sample: null

model:
  type: 'STEP'
  dataset_name: 'PEMS03'
  input_dim: 1
  output_dim: 1
  num_nodes: 358
  lag: 12
  horizon: 12
  
  # TSFormer参数
  tsformer_args:
    patch_size: 12
    in_channel: 1
    embed_dim: 96
    num_heads: 4
    mlp_ratio: 4
    dropout: 0.1
    num_token: 4032
    mask_ratio: 0.75
    encoder_depth: 4
    decoder_depth: 1
    mode: "forecasting"
  
  # GraphWaveNet后端参数
  backend_args:
    num_nodes: 358
    support_len: 2
    dropout: 0.3
    gcn_bool: true
    addaptadj: true
    aptinit: null
    in_dim: 2
    out_dim: 12
    residual_channels: 32
    dilation_channels: 32
    skip_channels: 256
    end_channels: 512
    kernel_size: 2
    blocks: 4
    layers: 2
  
  # 离散图学习参数
  dgl_args:
    dataset_name: 'PEMS03'
    k: 10
    input_seq_len: 12
    output_seq_len: 12

train:
  loss_func: mae
  seed: 10
  batch_size: 8
  epochs: 100
  lr_init: 0.002
  weight_decay: 1.0e-5
  lr_decay: true
  lr_decay_rate: 0.5
  lr_decay_step: [1, 18, 36, 54, 72]
  early_stop: true
  early_stop_patience: 15
  grad_norm: true
  max_grad_norm: 3.0
  real_value: true

test:
  mae_thresh: null
  mape_thresh: 0.0

log:
  log_step: 200
  plot: false