I am new in data science so please accept my apology in advance if my question sounds stupid. I want to do a time series forecast of outage mins in the current regulatory year. The regulatory year starts from 1 April and ends on 30 March of next year. I have data of around six months i.e. from April to September. Outage does not occur every day. So I have only 144 data points (or days out of 171 days) where the outage occurred. I have plotted the data in the following graph. The graph shows the cumulative sum of outage mins.
Now I am trying to predict the value from October to March. I wanted to forecast the value that what would be the cumulative outage mins by the end of March next year. I tried to use Exponential smoothing but it did not work, it may be because I don't have a lot of observation. Then I was reading about ARIMA but not sure whether its the right algorithm to use or not as I don't think that there would be any seasonality in this scenario and also I don't have long data points. Could anyone help with which algorithm should I use to forecast the value? I am using Python as a programming language. Any help would be really appreciated.