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I have been working on a similar kind of problem and below is my methodology. Hope it is of help:
Classify and analyze your time series problem. This will help narrowing down the domain of your problem and selection of technique. It can be done on the basis of factors including but not limited to:
Multivariate on univariate?
Regression or Classification?
...
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Sklearn has pipeline. If you have fit and transform attributes iteratively, you can make them pipeline by Pipeline class in sklearn.pipeline.
Read the docs:
https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html
Additionally you can save and load a pipeline object by joblib.dump and jublib.load.
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For larger projects snakemake is a way to go for Python (it extends Python syntax, valid Python is valid snakemake). It originates in bioinformatics and even has its own publication; it is widley adopted and used by many projects (see the literature list in the first link or the citations for the linked article).
For Jupyter notebook based projects, I made ...
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You can refer below the answer to the similar question asked.
https://stackoverflow.com/questions/14096520/compile-r-script-into-standalone-exe-file
Alternatively,
You can use R-shiny app for it. You can build application whih can be deployed on website also. See below link for the examples of application made by R-shiny.
https://shiny.rstudio.com/gallery/...
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There are a lot of great resources on time series which I found really handy examples with hands-on examples in R. Though, I myself even as a regular python user find these below mentioned articles and blogs easy, to begin with, Time Series Analysis in R. My suggestion is to go through the statistical models in Time Series like ARMA, ARIMA, etc., and then ...
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