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When we deal with many different multivariate time series, each time series can have a different number of variates. So "any-variate" means that the model is ab

by gorold 3y ago

When we deal with many different multivariate time series, each time series can have a different number of variates. So "any-variate" means that the model is able to take as inputs multivariate time series with arbitrary number of variates, and model the interactions with the Transformer's attention mechanism. This is something that many other TS foundation models do not consider yet - they convert all multivariate time series into multiple univariate time series. Whether or not the forecasts improves as a result of the additional covariates is still an open question which needs to be studied more -- we need to build better evaluations and benchmarks for this.

dcl 3y ago
Understood, thank you. There are certainly applications in demand sensing/demand forecasting where things like recent order information, recent sales, CRM inputs are quite predictive of near-term outcomes, but become useless for longer horizon forecasts. In my experience, when information like this is available, no time-series technique that is unable to leverage this information would beat even simple regressions for short term horizon forecasts.
lukas_b 3y ago
This looks very interesting! I'm trying to understand if the flattening technique might work for my ts. It's structured as follows: At each time step t, I have an m by n data matrix. The value for m (rows) varies per time step. n stays constant and represents the features. And i want to predict one of the n values. (In this case, t represents a single day, m (rows) represent the people that entered a store on that day, and n (cols) represent various features of the people. I want to predict one of those features, given the others.) The fact that it's a time series matters, because i expect the relationship to change over time. For instance some feature n[x] (person wears a yellow shirt) might be correlated with my target feature n[y] (person steals) but only in the summer. would it be possible to flatten this too? What would that look like?

by gorold 3y ago

dcl 3y ago

Understood, thank you. There are certainly applications in demand sensing/demand forecasting where things like recent order information, recent sales, CRM inputs are quite predictive of near-term outcomes, but become useless for longer horizon forecasts. In my experience, when information like this is available, no time-series technique that is unable to leverage this information would beat even simple regressions for short term horizon forecasts.

lukas_b 3y ago

This looks very interesting! I'm trying to understand if the flattening technique might work for my ts. It's structured as follows: At each time step t, I have an m by n data matrix. The value for m (rows) varies per time step. n stays constant and represents the features. And i want to predict one of the n values. (In this case, t represents a single day, m (rows) represent the people that entered a store on that day, and n (cols) represent various features of the people. I want to predict one of those features, given the others.) The fact that it's a time series matters, because i expect the relationship to change over time. For instance some feature n[x] (person wears a yellow shirt) might be correlated with my target feature n[y] (person steals) but only in the summer. would it be possible to flatten this too? What would that look like?