Row 6918
Content Data
This page contains data entry 6918 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.
Time series forecasting is super important for many industries, like retail, energy, finance, etc.
I delivered many projects in this area with statistical models, deep learning models (LSTM, CNN) and always it was a challenge.
With a great development in language model space I was thinking how LLM architecture could be used for forecasting and while I was exploring this idea I found that Amazon already delivered multiple **pretrained time series forecasting models** based on language model architectures.
If you are interesting check following resources:
[https://github.com/amazon-science/chronos-forecasting](https://github.com/amazon-science/chronos-forecasting)
[https://www.amazon.science/blog/adapting-language-model-architectures-for-time-series-forecasting](https://www.amazon.science/blog/adapting-language-model-architectures-for-time-series-forecasting)
What do you think, will a such models make a forecasting more accurate?
| Field | Value |
|---|---|
| text | Time series forecasting is super important for many industries, like retail, energy, finance, etc. I delivered many projects in this area with statistical models, deep learning models (LSTM, CNN) and always it was a challenge. With a great development in language model space I was thinking how LLM architecture could be used for forecasting and while I was exploring this idea I found that Amazon already delivered multiple **pretrained time series forecasting models** based on language model a… |
| label | r/machinelearning |
| dataType | post |
| communityName | r/MachineLearning |
| datetime | 2024-05-14 |
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Raw Record
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"text": "Time series forecasting is super important for many industries, like retail, energy, finance, etc. \n\nI delivered many projects in this area with statistical models, deep learning models (LSTM, CNN) and always it was a challenge. \n\nWith a great development in language model space I was thinking how LLM architecture could be used for forecasting and while I was exploring this idea I found that Amazon already delivered multiple **pretrained time series forecasting models** based on language model architectures. \n\n\n\nIf you are interesting check following resources: \n\n[https://github.com/amazon-science/chronos-forecasting](https://github.com/amazon-science/chronos-forecasting)\n\n[https://www.amazon.science/blog/adapting-language-model-architectures-for-time-series-forecasting](https://www.amazon.science/blog/adapting-language-model-architectures-for-time-series-forecasting)\n\n\n\nWhat do you think, will a such models make a forecasting more accurate? ",
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Entry Information
- Entry ID: 6918
- Repository: Axioma AXP
- Dataset: arrmlet/reddit_dataset_36
- Total Entries: 100,000