I need a hint on how to start developing a new `.ipynb` project for Jupyter Notebook on Time Series with a specific demands
I’m looking forStock Price Time Series Forecasting using Deep Convolutional/fully connected Networks/RNNcodefor forecasting the prices on stock marketsusing Python. The simplest possible code for this task with.ipynbextension will do.I cannot find one with google, any hints for downloading or creating it on my own ?EDITMore specifically, I need from data with one ormoretimes series-a daily data with prices, and myinput how many days from nowonand myprobability inputI needlower and upper boundhow much will the asset cost after this period: estimated min and max value.Or, in other wordsI need to compose the optimalportfoliofor these assets, leaving some money without buying anything: how much fraction of the initial amount of money should be intact on my account, among how much % shopuld I dedicate to n-th asset.EDITExample:say, I have this 2 time series:[1,2,4,3,2,2,2][3,3,3,3,4,4,8]and 100$ with initial amountshould yield this (by now just random, in reality that should make a sense and be derived from the 2 assets by statistical means and Deep Learning) probabilities:31$ of asset 1,49$ of asset 2,20$ leaving on my accountafter 4 days, all happening with probability 8%.Is there such a code .ipynb at best already available ?