In this work, we highlight our novel evolutionary sparse time-series forecasting algorithm also known as EvoSTS. The algorithm attempts to evolutionary prioritize weights of Long Short-Term Memory (LSTM) Network that best minimize the reconstruction loss of a predicted signal using a learned sparse coded dictionary. RESEARCH ARTICLE Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes Ali Caner Tu¨ rkmen ID 1‡*, Tim Januschowski1, Yuyang Wang2, Ali Taylan Cemgil3 1 Amazon Web Services AI Labs, Berlin, Germany, 2 Amazon Web Services AI Labs, East Palo Alto, CA, United States of America, 3 Department of Computer. In this paper, we present an original approach based on the MCM regressor, which builds sparse and accurate models for short-term time series forecasting. Results on a number of datasets establish that the proposed approach is superior to a number of state-of-the-art methods, and yields sparse models. Intermittency are a common and challenging problem in demand forecasting. We introduce a new, unified framework for building probabilistic forecasting models for intermittent demand time series.

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