I don't know much about the topic, but I want to learn.

I have been given a time series in the form of (date, number). and I need to forecast the values for a specific date (continuation of the time-series). What would be the correct procedure to do it and how can I forecast it?

So far I analyzed the input data:

my input is about 100K entries.

time interval is mostly 1 day interval, but is few cases it can be 1day +- few hours, and rarely 2days.

Q1: Should I eliminate the irregular entries? and then should I interpolate the missing data to have 100% regular input data? Or leave it the way it is and algorithm will treat it for me.

Q2: Should I keep the first entry as date? or is it more common to convert it as the time interval from the previous entry?

In the image below you see the crop of the data cycle, it shows 3 cycles(?). As you can see the cycle has some variation from one to the other and is repeated (with slight changes) in whole time series. the period of each cycle is about 150 days.

I also tried the TREND functions such as logarithmic and polynomial least square fitting, but they did not produce good results.

What are the passes that I should proceed from here?

NB. We do not have any information on the nature of the time series or what the numbers represent.

Thank you very much