role in determining the
Posted: Sat May 24, 2025 8:37 am
Firstly, the frequency (e.g., minutely, hourly, daily sampling rates) of time series plays a crucial patterns present in the data. However, cross-frequency learning poses challenges due to negative interference, with existing approaches typically circumventing this issue for multi-frequency datasets by training one model per frequency.
Secondly, time series data exhibit heterogeneity in terms of dimensionality, where multivariate time series may have varying numbers of variables. Moreover, each variable often measures a semantically distinct quantity across datasets. While treating each variable of a multivariate time series independently can mitigate this issue, a universal model should ideally be flexible enough to consider interactions between variables and account for exogenous covariates.
Thirdly, probabilistic forecasting is a critical requirement for many applications. However, different datasets possess varying support and distributional properties. For instance, using a afghanistan phone number list symmetric distribution (e.g., Normal, Student-T) as the predictive distribution may not be suitable for positive time series. Consequently, standard approaches that pre-define a simple parametric distribution may lack the flexibility needed to capture the diverse range of datasets effectively.
Lastly, the development of a large pre-trained model capable of universal forecasting necessitates a comprehensive dataset spanning diverse domains. Unfortunately, existing time series datasets are often insufficiently large and diverse to support the training of such models.
Our New Approach: Unified Training of Universal Time Series Forecasting Transformers.
Secondly, time series data exhibit heterogeneity in terms of dimensionality, where multivariate time series may have varying numbers of variables. Moreover, each variable often measures a semantically distinct quantity across datasets. While treating each variable of a multivariate time series independently can mitigate this issue, a universal model should ideally be flexible enough to consider interactions between variables and account for exogenous covariates.
Thirdly, probabilistic forecasting is a critical requirement for many applications. However, different datasets possess varying support and distributional properties. For instance, using a afghanistan phone number list symmetric distribution (e.g., Normal, Student-T) as the predictive distribution may not be suitable for positive time series. Consequently, standard approaches that pre-define a simple parametric distribution may lack the flexibility needed to capture the diverse range of datasets effectively.
Lastly, the development of a large pre-trained model capable of universal forecasting necessitates a comprehensive dataset spanning diverse domains. Unfortunately, existing time series datasets are often insufficiently large and diverse to support the training of such models.
Our New Approach: Unified Training of Universal Time Series Forecasting Transformers.