Time series forecasting uses historical, time-stamped data to create models that predict future events by identifying patterns in the data. This method analyzes trends, seasonality, and other fluctuations over time to anticipate outcomes, improve decision-making, and reduce risks in fields like business, finance, weather prediction, and resource allocation.
100,000 time series from diverse domains (finance, demographic, macro, micro, industry, other). Competition ran in 2018. Lower sMAPE/MASE/OWA is better.
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6 datasets tracked for this task.
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