darts

s. I don't intend to sound demeaning here, really. But I'm trying to understand what the point is. This doesn't look like someone's weekend project, but we already have plenty of established projects which tackle this effectively. There are three major markets for time series work: 1. You're an analyst with a lot of domain knowledge who needs to analyze daily, seasonal data but you don't have a strong statistical or engineering background. This person should probably just choose Prophet (again, the developers of Prophet explicitly acknowledge that it's designed for scalable good enough models by non-stats people, not for the best model given the data). 2. You're a data scientist with a good statistical background and you need to produce forecasts. You can afford to dig into what the model is doing and select a model based on a series of diagnostics and knowledge about the data itself. This person should probably choose a more complete suite, like Darts. The important thing here is developing good models quickly while being able to do more than just press a button. 3. You're a data scientist (or statistician) which a very strong statistical background who needs to produce the best model they can for answering a specific question. This person is probably going to use R, Stan, Statsmodels or PyMC to come up with something bespoke. They may or may not need to systematize it, but they don't need to produce quantity over quality. How does this thing improve the state of the art for any of these markets? -- 1 https://github.com/unit8co/darts 2 https://www.statsmodels.org/stable/index.html 3 https://peerj.com/preprints/3190/