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I set up a sensor which measures temperature data every 3 seconds. I collected the data for 3 days and have 60.000 rows in my csv export. Now I would like to forecast the next few days. When looking at the data you can already see a "seasonality" which displays the fridges heating and cooling cycle so I guess it shouldn't be too difficult to predict. I am not really sure if my data is too granular and if I should do some kind of undersampling. I thought about using a seasonal ARIMA model but I am having difficulties because of the size of my dataset. As the seasonality in the data is pretty obious is there maybe a model that fits better? Please bear with me I'm pretty new to machine learning.

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  • $\begingroup$ A good start would indeed be to try using time series models such as (S)ARIMA(X) models. When you say you have difficulties because of the dataset size, what exactly do you mean with this? $\endgroup$
    – Oxbowerce
    Jan 4 at 16:28
  • $\begingroup$ It's mostly because im unexperienced with the models. For ARIMA the examples I found use way smaller datasets and I'm not really sure how to change the paramaters according to my bigger dataset. For example when using AFC I'm not sure how I need to change 'lags' accordingly. $\endgroup$
    – Julia
    Jan 4 at 17:07

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