I am going to defined the readme a little
- they often don’t support missing/corrupt data
Clean data would benefit most models, not just non-deep learning models. Missing data introduces bias even in DL models.
- they focus on linear relationships and not complex joint distributions
1. If seasonality is present. Which is usually the case in practical business problems then you will find that actual ~ lag_actuals explains most of the variance with a linear relationship. non-linearities in time series is not something that I see often. You can usually make a feature that explains those away linearly.
- they focus on fixed temporal dependence that must be diagnosed and specified a priori
Not sure sure you are saying here.
- they take as input univariate, not multiple interval, data
That is not the case. Time series regression can take many lags for inputs.
- they focus on one-step forecasts, not long time horizons
False. Time series regression models are used to forecast revenue many years into the future in the business world.
- they’re highly parameterized and rigid to assumptions
So is F=ma
- they fail for cold start problems
Because a cold start is not a time series data set. Why would time series methods work on non time series data.
- they often don’t support missing/corrupt data
Clean data would benefit most models, not just non-deep learning models. Missing data introduces bias even in DL models.
- they focus on linear relationships and not complex joint distributions
1. If seasonality is present. Which is usually the case in practical business problems then you will find that actual ~ lag_actuals explains most of the variance with a linear relationship. non-linearities in time series is not something that I see often. You can usually make a feature that explains those away linearly.
- they focus on fixed temporal dependence that must be diagnosed and specified a priori
Not sure sure you are saying here.
- they take as input univariate, not multiple interval, data
That is not the case. Time series regression can take many lags for inputs.
- they focus on one-step forecasts, not long time horizons
False. Time series regression models are used to forecast revenue many years into the future in the business world.
- they’re highly parameterized and rigid to assumptions
So is F=ma
- they fail for cold start problems
Because a cold start is not a time series data set. Why would time series methods work on non time series data.