Timeline for Why LSTM models do not require labels for each step?
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
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| May 23, 2019 at 9:25 | comment | added | DataFramed | No, this is a wrong conclusion. Time-series models use historical data points as dependent to predict future points ( forecast). | |
| May 23, 2019 at 2:11 | comment | added | nolwww | And then, we can say that here, it’s not a time series problem, but just a prediction based on several days of data for each point. | |
| May 23, 2019 at 1:54 | comment | added | nolwww | Thanks for your answer. The difference is that, most of the time, in time series, we only use the price and the date as variables. Here, there are a lot of features. So, I was wondering if I should incorporate the labels of the past days, or only the labels of the last day? For example, if I want to predict a temperature, and I have 10 features, and seven days. Should I use the temperature of the six first days in my LSTM model, or only the seventh day temperature as label?? | |
| May 22, 2019 at 10:41 | history | answered | DataFramed | CC BY-SA 4.0 |