Hi all, I've been experimenting with timegpt api for a while. it is pretty straight forward to use the api and am able to generate predicting on my asset price data. I have ml expernece with non-time series data before , some special about time series 1. do i need to worry about choosing the best lookback period before giving the data to timegpt.forecast? or i just feed all the historical data i have (eg. 3 year daily closing price). if I need to decide a best lookback period, instead of trying random numbers and test blilndy, should I look at the historical forecast charts and performance metrics first, and decompose the trend , seasonality of my data. and then to decide to the best lookback period ? 2. i run grid search on a few parameters and measure a few metrics, for example

lookback_period=[60,90,365]
finetune_steps =[10,50,100]
pred_horizon=[7,14,30]
freq='D'

should i grid search all other paramters to find the best combination first, and then adjust the finetune_steps . Or is it ok to do the all at once?