63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
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import torch
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from torch import nn
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from layers.Transformer_EncDec import Encoder, EncoderLayer
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from layers.SelfAttention_Family import FullAttention, AttentionLayer
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from layers.Embed import PatchEmbedding
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import timesfm
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class Model(nn.Module):
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def __init__(self, configs):
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"""
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patch_len: int, patch len for patch_embedding
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stride: int, stride for patch_embedding
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"""
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super().__init__()
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self.model = timesfm.TimesFM_2p5_200M_torch.from_pretrained("google/timesfm-2.5-200m-pytorch")
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self.model.compile(
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timesfm.ForecastConfig(
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max_context=configs.seq_len,
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max_horizon=configs.pred_len,
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normalize_inputs=True,
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use_continuous_quantile_head=True,
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force_flip_invariance=True,
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infer_is_positive=True,
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fix_quantile_crossing=True,
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)
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)
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self.task_name = configs.task_name
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self.seq_len = configs.seq_len
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self.pred_len = configs.pred_len
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def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
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means = x_enc.mean(1, keepdim=True).detach()
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x_enc = x_enc.sub(means)
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stdev = torch.sqrt(
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torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
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x_enc = x_enc.div(stdev)
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B, L, C = x_enc.shape
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device = x_enc.device
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x_enc = torch.reshape(x_enc, (B*C, L))
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output, _ = self.model.forecast(
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horizon=self.pred_len,
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inputs=x_enc.cpu().numpy()
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)
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output = torch.Tensor(output).to(device)
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dec_out = torch.reshape(output, (B, output.shape[-1], C)).to(x_enc.device)
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dec_out = dec_out * \
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(stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))
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dec_out = dec_out + \
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(means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))
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return dec_out
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def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
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if self.task_name == 'zero_shot_forecast':
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dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
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return dec_out
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return None
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