parser.add_argument('--pred_len',type=int, default=24,help='prediction sequence length')# Informer decoder input: concat[start token series(label_len), zero padding series(pred_len)]parser.add_argument('--enc_in',type=int, default=7,help='encoder input size') parser.add_argument('--de...
Module): def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu"): super(EncoderLayer, self).__init__() d_ff = d_ff or 4*d_model self.attention = attention self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1) self.conv...
class Encoder(nn.Module): def __init__(self, input_size, d_model, n_heads, e_layers, d_ff, dropout): super().__init__() self.input_size = input_size self.d_model = d_model self.n_heads = n_heads self.e_layers = e_layers self.d_ff = d_ff self.dropout = dropout self...
args.enc_in =5# encoder input sizeargs.dec_in =5# decoder input sizeargs.c_out =1# output sizeargs.factor =5# probsparse attn factorargs.padding =0# padding typeargs.d_model =256# dimension of modelargs.n_heads =4# num of headsargs.e_layers =2# num of encoder layersargs.d_lay...
# Informer decoder input: concat[start token series(label_len), zero padding series(pred_len)] # 编码器default参数为特征列数 parser.add_argument('--enc_in', type=int, default=7, help='encoder input size') # 解码器default参数与编码器相同 ...
parser.add_argument('--enc_in', type=int, default=7, help='encoder input size') #用于指定Informer编码器的输入大小。 1. 2. parser.add_argument('--dec_in', type=int, default=7, help='decoder input size') #用于指定Informer解码器(或任何其他解码器组件)的输入大小 1. 2. parser.add_arg...
第二个AttentionLayer对应的就是交叉的全注意力（Encoder的输出作为key和value，上一层Decoder的输出作为...
我目前在用informer做时间序列预测的时候也遇到了类似的问题，短期的预测效果还不如简单的ARMA模型，且...
1. Encoder: Allowing for processing longer sequential inputs under the memory usage limitation encoder被设计用来抽取鲁棒的长序列输入的long-range依赖,在第 个序列输入 被转为矩阵 Self-attention Distilling 作为ProbSparse Self-attention的自然结果,encoder的特征映射会带来 值的冗余组合,利用distilling对具有支配...