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**Describe the bug**
N-Beats and xgboost models are taking more time when executing in parallel. If I am using single process, each algorithm is executing within one sec. Where as If I increase proce…
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**Describe the bug**
Predict covariates does not work for XGBOOST/ CATBOOST/LIGHTGBM
**To Reproduce**
Attached is the excel file that has sample data - I did spend time going through darts.utils…
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用MLP/LSTNet等模型进行paddlets训练时添加了已知列(known_cols),但在调用模型预测时,发现已知列的数据对预测结果没有任何影响,甚至待预测数据中没有已知列也不会报错。
`
tsDataset = TSDataset.load_from_dataframe(
df,
time_col='monitorTime', target_cols='…
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### What happened + What you expected to happen
Hi, I'm a beginner on deep learning time-series forcasting and really appreicate about the work on neuralforcast which provide the strong function and …
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Exception: Horizon `h=1` incompatible with `seasonality` or `trend` in stacks
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I am using NBEATS model for forecasting. Since we are in a trial phase we are giving past data and generating forecast from the same and we are repeating these steps using a for-loop. We are predictin…
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I might have misunderstood something, but has the feature_importance functionality been removed in the darts implementation? If that is the case, what is the best way to achieve the same thing?
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**Describe the bug**
Hello,
Very likely not a bug but a question(?)
I'm trying to code a ML model that would be able to make some short term predictions based on financial data, which looks l…
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在LSTNetRegressor训练的时序模型中,经过带已知协变量训练后,模型预测却失效的问题
版本信息:
PaddlePaddle: 2.3.2.post112
paddleTs: 1.1.0
模型训练代码:
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sk…
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I trained the model and used the **historical_forecasts** method to predict the validation series and after it I used method **predict** to do the same and had different results. with different R2 an…