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🔍 Problem Description:
The rapid advancement of technology and online platforms has led to a significant increase in the amount of data generated. Predicting sales trends can be a complex challenge f…
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How to forecast multivariate series?
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HOW should we treat the data differently, given it's a time-cts climite prediction problems.
Helpful posts:
- https://www.kaggle.com/competitions/widsdatathon2023/discussion/376574
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https://www…
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**Is your feature request related to a problem? Please describe.**
sktime currently lacks built-in tools for model explainability, making it difficult for users to interpret and understand the pred…
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@titzenic
FYI: Es wird eine neue Spalte "rate_of_change" hinzugefügt
Bei allen Aufgaben ist Visualisierung mit inbegriffen. Für Visualisierung entweder Matplotlib oder (bevorzugt) Seaborn.
Mögli…
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您好，作者，感谢提供如此完整的学习框架！本人在使用和移植基线的过程中遇到一些问题和不便的地方，在此提出来以便您参考优化。
声明：以下问题和建议仅代表个人看法，仅供参考
问题：利用pycham直接运行data_preparation显示找不到数据集文件，运行train时也一样，做如下修改就可以运行：
OUTPUT_DIR = "../../../experiments/datasets…
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- Is there a "right way" to handle missing values for this model?
- How should I pad when time series have non-uniform lengths in a batch?
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@jdb78
I used 150,000 pieces of data to create a dataset,add'series' and 'time_idx' column like this:
```
.......
data_len=150000
max_encoder_length = 4*96
max_prediction_length …
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Objective: Apply a traditional approach using different models such as Auto Regression, Moving Average, Random walk to do time series analysis on different stock datasets and prediction for the datas…
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I am using this model on stock companies to predict their price of next ten days . Does TimesFm support time series prediction with multiple input features? Along with price, can we give input feature…