Forecasting financial short time series
using classical exponential smoothing methods. However, this shortcoming is compensated by the the size of our dataset: millions of time series. The latter allows tackling the problem of time series prediction from a pattern recognition perspective. Specifically, we propose a method for short time series
prediction based on time series clustering and distance-based regression. We experimentally show that this strategy leads to improved accuracy compared to exponential smoothing methods. We additionally describe the underlying big data platform developed to carry out the forecasts in an efficient manner (comparisons to millions of elements in near real-time).
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