arXiv:2310.20545v3 Announce Type: replace Abstract: We present a multi-task optimization approach based on a deep learning architecture for time series forecasting. We leverage large collections of time series to identify the weights of forecasting models that can be combined to produce forecasts for each series. This method jointly addresses two tasks: the selection of different forecasting models, and their effective combination. In doing so, it keeps into account, in an original way, both the accuracy and diversity of the forecasting methods. For a given time series, the model combination m
The continuous advancements in AI and deep learning architectures are enabling more sophisticated approaches to time series forecasting, pushing the boundaries of predictive analytics.
Improving the accuracy and diversity of forecast combinations for time series has broad implications across various industries dependent on predictive models, from finance to supply chains.
This multi-task optimization approach offers a more robust and adaptable framework for forecasting, potentially leading to more reliable predictions and better decision-making under uncertainty.
- · Financial services
- · Supply chain logistics
- · Energy utilities
- · Deep learning practitioners
- · Traditional statistical forecasting models
- · Businesses reliant on single-model forecasts
Enhanced predictive accuracy across numerous operational domains.
Increased automation of forecasting tasks and reduced need for manual model selection.
New business models emerging around optimized predictive intelligence services.
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Read at arXiv cs.LG