81–85 of 85 posts
What is the point of this kind of comparison? It is completely dependent on the 3000 datasets they chose to use. You're not going to find that one method is better than another in-general or find some type of time series for which you can make a specific methodological recommendation (unless that series is specifically constructed with a mathematical feature, like stationarity). What matters is "which method is bette…
Earlier quoted context omitted.
A point estimate of distribution parameters describing a population is frequentist. A point estimate of distribution parameters describing another distribution's parameter is Bayesian.
How the parameters are estimated it not the message. In statistics there are latin letters and greek letters. When you see a symbol denoted as a greek letter then that is a population parameter. When you see a latin letter that is a sample estimate. It could be Frequentist, Bayesian, Likelihoodist, Fiducial, Empirical Bayes, etc. Theoretical population greeks or sample calculated latins.
Earlier quoted context omitted.
My understanding is that the biggest advantage between traditional machine learning and neural networks is that neural networks are useful when features either need to be generated or are poorly understood (such as a binary blob of an image or sound sample). So for data that is already neatly organized and labeled in a spreadsheet, DL loses its main advantage.
How do you refer to traditional ML, DL and neutral networks in this context? Realize they are rather nebulously defined IRL
Earlier quoted context omitted.
How the parameters are estimated it not the message. In statistics there are latin letters and greek letters. When you see a symbol denoted as a greek letter then that is a population parameter. When you see a latin letter that is a sample estimate. It could be Frequentist, Bayesian, Likelihoodist, Fiducial, Empirical Bayes, etc. Theoretical population greeks or sample calculated latins.
Now that's a fascinating and deliciously crackpot theory.
Seems like these guys just wasted $11k to erroneously claim, “deep learning bad! Simple is better!” There’s definitely use for these classical, model-based methods, for sure. But a contrived comparison claiming they’re king is just misinformation. Eg, here are a number of issues with classical techniques where dl succeeds (‘they’ here refers to classical techniques): - they often don’t support missing/corrupt data -…
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 expl…
- You write: 1. "Clean data would benefit most models, not just non-deep learning models. Missing data introduces bias even in DL models." and 2. "Because a cold start is not a time series data set. Why would time series methods work on non time series data."
Clean data is good, yes, thanks for the insight. But 1. missing data is a huge issue for model quality, particularly for highly-parameterized state space models if you're missing key aspects (e.g. an ETS model with missing data for trend is an awful model) and 2. cold start is certainly a time series concept (and one with lots of ongoing research at that). Here's an example: At Amazon, thousands of new products launch each week. Many of those new products have zero or very limited historical data, but metrics (e.g. demand) still need to be forecasted. Despite your claim that such an issue doesn't exist, this is a cold start time series problem. Modern techniques exist to handle this issue, such as DeepAR, which handles the cold start issue by training a global model for probabilistic forecasts (https://arxiv.org/pdf/1704.04110.pdf)
- You write: "That is not the case. Time series regression can take many lags for inputs"
Of course, but that's not what was written. I suggest you google the difference between multiple and multivariate time series
- You write: "False. Time series regression models are used to forecast revenue many years into the future in the business world"
In the 'business world'(?) you can use a forecasting model to do multi-step ahead prediction (aka k-step ahead error propagation), but I'm referring specifically to direct multi-horizon forecasting (e.g. https://arxiv.org/pdf/1711.11053.pdf)
- You write: "Not sure sure you are saying here."
I'm describing verbatim the process by which the classical forecasting techniques described work.
- You write: "So is F=ma"
This is agreeing with me. I think maybe your intent was to write something with a nonlinearity, but I’m not sure you’re sure what point you’re trying to make here.
- You write: "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."
You ignored the bit about joint distributions and you gave an example of lag features you've seen. Non-linearities are common in many time series applications (e.g. weekly returns of foreign exchange rate , realized volatility). You can handle them with classical techniques, but it's painful if you do something more complex than just introducing a feature and hope it captures the missing variance.