from transformers import TimeSeriesTransformerConfig, TimeSeriesTransformerForPrediction config = TimeSeriesTransformerConfig(prediction_length=prediction_length,# context length:context_length=prediction_length * 2,# lags coming from helper given the freq:lags_...
在Transformer的基础上构建时序预测能力可以突破以往的诸多限制,最明显的一个增益点是,Transformer for TS可以基于Multi-head Attention结构具备同时建模长期和短期时序特征的能力。 本文将要介绍的一个充分利用了Transformer的优势,并在Transformer的基础上改进了Attention的计算方式以适应时序数据,同时提出了一种解决Transformer...
defsplit_sequence(sequence:np.ndarray,ratio:float=0.8)->tuple[torch.Tensor,torch.Tensor,torch.Tensor]:"""Splits a sequence into 2 (3) parts, as is required by our transformermodel.Assume our sequence length is L, we then split this into src of length Nand tgt_y of length M, with N ...
from transformers import AutoformerConfig, AutoformerForPrediction config = AutoformerConfig.from_pretrained("kashif/autoformer-traffic-hourly") model = AutoformerForPrediction.from_pretrained("kashif/autoformer-traffic-hourly") test_dataloader = create_test_dataloader( config=config, freq=freq, data...
在AAAI 2023 的论文 Are Transformers Effective for Time Series Forecasting? 中,作者声称 transformer 系列模型在时间序列预测方面并不有效。他们拿基于 transformer 的模型与一个简单的线性模型 DLinear 作对比。DLinear 使用了 Autoformer 中的 decomposition layer 结构 (下文将会介绍),作者声称其性能超越了基于 tr...
ThepositionEmbeddingLayerallows for the encoding of the position information of each element within the sequence. By incorporating position embedding, the model can learn to differentiate between different time steps and capture time dependencies within the data. ...
Many recent works have attempted to address this problem by using Transformer encoders11,12,13. Transformer designed for sequence-to-sequence prediction14originally, it’s a model based on self-attention (SA). SA is a core part of Transformer. Due to SA's ability to model the correlation bet...
for i in range(len(X) - seq_length): sequences.append(X[i:i + seq_length]) targets.append(X[i + seq_length]) return np.array(sequences), np.array(targets) # 数据生成 seq_length = 50 X, y = generate_synthetic_data(n_samples=2000, seq_length=seq_length) ...
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability to capture precise long-range dependency coupling between output...
finding shortest path for a fixed graph (left) posed as reinforcement learning. Training dataset consists of random walk trajectories and their per-node returns-to-go (middle). Conditioned on a starting state and generating largest possible return at each node, Decision Transformer sequences optimal...