How to pass time series data to SARIMA, ARIMA, SARIMAX, etc
How to pass time series data to SARIMA, ARIMA, SARIMAX, etc
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Kriti · External communityPost link
External question — Data Science Stack Exchange
Author: Kriti
Original post: https://datascience.stackexchange.com/questions/121406
License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/
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I am trying to predict stock price of a company, the data is non stationary.
Steps I followed -
Analyze the raw data
Determine whether the raw time series data is stationary or not using ADF and KPSS
Applied first differencing and seasonal differencing to make the data stationary
Determine the MA and AR lags using the stationary data by plotting ACF, PACF plots
My question is should I pass raw data (non-stationary, from Step 1) to time series model like SARIMA, ARIMA and SARIMAX and use the stationary data(Step 3) to determine MA and AR lag coefficients for the model
OR
I should pass the stationary data(Step 3) to the time series model like SARIMA, ARIMA, SARIMAX, etc. and use the MA and AR lag coefficients for the model. And then to determine the predicted original time series , I should undo all the transformations that I did in Step 3 to make the time series data stationary.
Thank you for your help
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brewmaster321 · External communityPost link
External answer — Data Science Stack Exchange
Author: brewmaster321
Original post: https://datascience.stackexchange.com/a/121417
License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/
Adaptation: HTML converted to plain text; contact email addresses removed.
In general, you're going to use the data as is to fit the model, but use the data analysis to choose/validate/understand your parameters (p,d,q,P,D,Q, etc). For the most part, it's advisable to do a grid search on your parameters anyway to get the best fitting model, but having some intuitive understanding of where to set the grid search limits will always help.
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Quoted from Forex.com.bd-Editorial External question — Data Science Stack Exchange Author: Kriti Source score (net votes, not local likes): 0 Original post: https://datascience.stackexchange.com/questions/121406 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I am trying to predict stock price of a company, the data is non stationary. Steps I followed - Analyze the raw data Determine whether the raw time series data is stationary or not using ADF and KPSS Applied first differencing and seasonal differencing to make the data stationary Determine the MA and AR lags using the stationary data by plotting ACF, PACF plots My question is should I pass raw data (non-stationary, from Step 1) to time series model like SARIMA, ARIMA and SARIMAX and use the stationary data(Step 3) to determine MA and AR lag coefficients for the model OR I should pass the stationary data(Step 3) to the time series model like SARIMA, ARIMA, SARIMAX, etc. and use the MA and AR lag coefficients for the model. And then to determine the predicted original time series , I should undo all the transformations that I did in Step 3 to make the time series data stationary. Thank you for your help
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