I have a time series dataset project (single variable time series) on market share changes of a particular product in a region (values are recorded every day from 2018 to 2022) where I need to predict whether the share values will show an upward or downward trend long-term (1 year). I will be using Python for the project.
I have been searching for various methods such as traditional ARIMA, random forest, and also LSTM but I'm not sure which would be best for forecasting long-term trends. I know that predicting exact values for long term isn't feasible due to performance issues, but my objective here is to just forecast a 'directional' long-term trend. So my task is basically trend analysis using time series methods.
Is there a good method to forecast trends among the ones mentioned above? Or is there possibly a better method I don't know? I would appreciate any insight into the issue.