Neutral mention · rule
I appreciate the response but I admit I'm a bit confused - maybe it's the case that you're not familiar with the need for modern forecasting? Some of your 'defense' is just flat out wrong. A few points: - 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…
Read the full comment
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 <currency> foreign exchange rate , <stock> 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.