What are exogenous variables in time series?
In time series, the exogenous variable is a parallel time series that are not modeled directly but is used as a weighted input to the model. The method is suitable for univariate time series with trend and/or seasonal components and exogenous variables.
What is the dependent variable in time series?
1.1 Univariate Time Series A univariate time series, as the name suggests, is a series with a single time-dependent variable. For example, have a look at the sample dataset below that consists of the temperature values (each hour), for the past 2 years. Here, temperature is the dependent variable (dependent on Time).
What is meant by a VAR 1 model?
In the VAR model, each variable is modeled as a linear combination of past values of itself and the past values of other variables in the system. Since you have multiple time series that influence each other, it is modeled as a system of equations with one equation per variable (time series).
Can you add extra features to time series model?
Maybe the LSTM will work anyway, but even if it does, it will probably come at the cost of higher loss / lower accuracy per training time. Alternatively, you can introduce this sort of “extra” information into your model outside of the LSTM by means of additional layers.
When to add v to a time series model?
For the very first timestep, add v → to the hidden state of the RNN when calculating its value. This approach is the most “theoretically” correct, since it properly conditions the RNN on your non-temporal inputs, naturally solves the shape problem, and also avoids polluting your inputs timesteps with additional, non-temporal information.
Is the weather a feature of a time series?
In my mind, weather is a time series feature: where I live, it happens to be a function of time. So it would be quite reasonable to encode weather information as one of your features in each timestep (with an appropriate encoding, like cloudy=0, sunny=1, etc.).