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.