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Update README.md
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README.md
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num_examples: 411232
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download_size: 311064786
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dataset_size: 566804417
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---
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# Cross Validated / stats.stackexchange.com
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@@ -102,7 +111,9 @@ for sample in iter(ds): print(sample["Body"])
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The original Data Dump formats the "Body" field as HTML, using tags such as `<code>`, `<h1>`, `<ul>`, etc.
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This HTML format has been converted to Markdown following [mikex86/stackoverflow-posts](https://huggingface.co/datasets/mikex86/stackoverflow-posts) conversion rule.
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After differencing I saw that my constant/intercept is not statistically significant. Does anybody know how to fit the same model without the const term?
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im using statsmodels.tsa.arima.model
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To give a relative example I have: `ARIMA(data, order=(3,0,0))` an AR(3) model and say it that the second coefficient is insignificant. I can get rid of it by typing
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```
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but how can I get rid of coefficient??
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num_examples: 411232
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download_size: 311064786
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dataset_size: 566804417
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language:
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- code
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- en
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task_categories:
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- question-answering
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- text-generation
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- text2text-generation
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tags:
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- code
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---
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# Cross Validated / stats.stackexchange.com
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The original Data Dump formats the "Body" field as HTML, using tags such as `<code>`, `<h1>`, `<ul>`, etc.
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This HTML format has been converted to Markdown following [mikex86/stackoverflow-posts](https://huggingface.co/datasets/mikex86/stackoverflow-posts) conversion rule.
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**Example:**
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After differencing I saw that my constant/intercept is not statistically significant. Does anybody know how to fit the same model without the const term?
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im using statsmodels.tsa.arima.model
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To give a relative example I have: `ARIMA(data, order=(3,0,0))` an AR(3) model and say it that the second coefficient is insignificant. I can get rid of it by typing
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```
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but how can I get rid of coefficient??
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