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Add forecasting notebook #20

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Apr 7, 2021
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@Shreyanand Shreyanand commented Mar 23, 2021

Related Issues and Dependencies

#9

This introduces a breaking change

  • Yes
  • No

Description

In this notebook, I compare several forecasting techniques for an example dataset of a cluster metric.

@sesheta sesheta added the do-not-merge/work-in-progress Indicates that a PR should not merge because it is a work in progress. label Mar 23, 2021
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@sesheta sesheta added the size/XXL Denotes a PR that changes 1000+ lines, ignoring generated files. label Mar 23, 2021
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sesheta commented Mar 23, 2021

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notebooks/ts-4-forecasting.ipynb#In[2]:10:1: F401 'statsmodels.tsa.stattools.adfuller' imported but unused
notebooks/ts-4-forecasting.ipynb#In[2]:11:1: F401 'statsmodels.tsa.seasonal.seasonal_decompose' imported but unused
notebooks/ts-4-forecasting.ipynb#In[2]:12:1: F401 'statsmodels.graphics.tsaplots as sgt' imported but unused
notebooks/ts-4-forecasting.ipynb#In[2]:13:1: F401 'statsmodels.tsa.arima_model.ARMA' imported but unused
notebooks/ts-4-forecasting.ipynb#In[2]:17:1: F401 'scipy.stats.distributions.chi2' imported but unused
notebooks/ts-4-forecasting.ipynb#In[2]:18:1: F401 'numpy as np' imported but unused
notebooks/ts-4-forecasting.ipynb#In[2]:21:1: F401 'itertools' imported but unused
notebooks/ts-4-forecasting.ipynb#In[133]:2:20: F821 undefined name 'auto_arima_model'
notebooks/ts-4-forecasting.ipynb#In[156]:2:2: N816 variable 'pred_ARIMA_312' in global scope should not be mixedCase
notebooks/ts-4-forecasting.ipynb#In[156]:3:2: N816 variable 'pred_train_ARIMA_312' in global scope should not be mixedCase
notebooks/ts-4-forecasting.ipynb#In[159]:2:2: N816 variable 'pred_ARIMA_313' in global scope should not be mixedCase
notebooks/ts-4-forecasting.ipynb#In[159]:3:2: N816 variable 'pred_train_ARIMA_313' in global scope should not be mixedCase
notebooks/ts-4-forecasting.ipynb#In[161]:4:2: N816 variable 'pred_SARIMA_3133134' in global scope should not be mixedCase
notebooks/ts-4-forecasting.ipynb#In[161]:5:2: N816 variable 'pred_train_SARIMA_3133134' in global scope should not be mixedCase

@Shreyanand Shreyanand linked an issue Mar 23, 2021 that may be closed by this pull request
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@Shreyanand Shreyanand changed the title [WIP] Forecasting notebook Add forecasting notebook Apr 7, 2021
@sesheta sesheta removed the do-not-merge/work-in-progress Indicates that a PR should not merge because it is a work in progress. label Apr 7, 2021
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/approve

@MichaelClifford MichaelClifford added the approved Indicates a PR has been approved by an approver from all required OWNERS files. label Apr 7, 2021
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sesheta commented Apr 7, 2021

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@sesheta sesheta merged commit fbbadea into aicoe-aiops:master Apr 7, 2021
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chauhankaranraj commented on 2021-04-07T14:27:27Z
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Is there a particular reason for using these set of params i.e. (3, 1, 2) and (3, 1, 3) for this particular dataset, or is it more like trial and error? In either case, should we mention somewhere why these were chosen?


Shreyanand commented on 2021-04-07T14:49:07Z
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Yup, so in the last notebook, we looked at how to find these parameters and we found these ones to have better AICs than the rest. Maybe I could add that in the markdown somewhere.

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chauhankaranraj commented on 2021-04-07T14:27:28Z
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Should this cell have been a markdown-type cell?


Shreyanand commented on 2021-04-07T14:50:21Z
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Yes, sloppy :D

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chauhankaranraj commented on 2021-04-07T14:27:28Z
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Same thing here, should the reasoning for selecting (3, 1, 3) and (3, 1, 3, 4) be mentioned here?


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@Shreyanand looks like the PR got merged before I could submit review 😢

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chauhankaranraj commented on 2021-04-07T14:27:28Z

Same thing here, should the reasoning for selecting (3, 1, 3) and (3, 1, 3, 4) be mentioned here?

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I think it will be valuable to add reasoning since these parameters might change once we change the dataset, say next month.

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Yup, so in the last notebook, we looked at how to find these parameters and we found these ones to have better AICs than the rest.


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Yes, sloppy :D


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Create a notebook comparing and describing techniques for forecasting time series
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