prediction def prediction(model, dl, t0, future): # 预测前先load model, dl就是待预测数据,t0就是前n和时间点,future就是要预测的n个时间点 # 比如你要用一周内前五天的数据训练模型,来预测后两天的值 t0 = 5 * 24 = 120, future = 48 with torch.no_grad(): predictions = [] observations ...
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_s...
from transformers import TimeSeriesTransformerConfig, TimeSeriesTransformerForPrediction 请注意,与 Transformers 库中的其他模型类似,TimeSeriesTransformerModel 对应于没有任何顶部前置头的编码器-解码器 Transformer,而 TimeSeriesTransformerForPrediction 对应于顶部有一个分布前置头 (distribution head) 的 TimeSeriesTrans...
pred_length=config.prediction_length, ), # step 5: add another temporal feature (just a single number) # tells the model where in the life the value of the time series is # sort of running counter AddAgeFeature( target_field=FieldName.TARGET, output_field=FieldName.FEAT_AGE, pred_length...
Inverted Transformers are Effective for Time Series Forecasting 论文作者: Yong Liu , Tengge Hu , Haoran Zhang , Haixu Wu, Shiyu Wang, Lintao Ma, Mingsheng Long 编译:Sienna 审核:Los 导读: iTransformer是2024年时间序列预测领域的最新研...
Deep Learning in Quantitative Finance: Transformer Networks for Time Series Prediction - matlab-deep-learning/transformer-networks-for-time-series-prediction
Time series prediction is important regardless of whether it is univariate or multivariate, and univariate ARIMA to multivariate autoregressive model VAR etc. have been used as machine learning techniques for a long time, and deep learning such as RNN and CNN are used for time-series recently. In...
Time Series Visualization using Transformer for Prediction of Natural Catastrophedoi:10.21275/SR211022155010Shivam PandeyMahek Jain
) - max_encoder_length]last_data = data[lambda x: x.time_idx == x.time_idx.max()]decoder_data = pd.concat( [last_data.assign(date=lambda x: x.date + pd.offsets.MonthBegin(i)) for i in range(1, max_prediction_length + 1)], ignore_index=True,)decoder_data["time_idx...
在预训练方法上，文章将单序列切分为序列片段，每个片段作为一个“词”，采用与LLM类似的下一词预测（Next Token Prediction, NTP）进行预训练。推理时，模型可通过自回归生成任意长度的序列。模型结构：剑走偏锋的仅解码器结构 不同于当下时序领域流行的仅编码器结构，Timer采用GPT风格的仅解码器Transformer。作者团队...