For business users, unfortunately none on hand at the moment. That being said, one of the biggest issues companies seem to encounter is that forecasting requir

For business users, unfortunately none on hand at the moment. That being said, one of the biggest issues companies seem to encounter is that forecasting requires logging of at least several years of business operations, especially if the target variable is highly seasonal. The need for sufficient historical data can make it difficult to commit to a new forecasting initiative at the drop of a hat. There first needs to be a culture of retaining as much data possible, since even external variables from other business areas may be useful in making predictions. You can get around this somewhat with synthetic data and piecing together alternative data sources, but it's not ideal. I would also say the ease of implementing a forecasting solution on a given dataset is inversely proportional to the value it provides. They're mainly useful for situations in which there are either too many independent time series for a group of human business analysts to track, or for situations in which the target variable is unruly for some reason. For forecasting problems that are simple enough to be modeled by state space methods or BSTS, those methods won't be able to provide results that are all that superior to what an experienced analyst could produce, since the series data itself is already quite predictable. Edit: Also, there is no one-size-fits-all approach when it comes to time series, and anyone who tells you differently is trying to sell you something. Time series is weird within ML because each dataset has unique properties that necessitates the use of potentially wildly different modeling techniques.