• A benchmark study on time series clustering - ScienceDirect
  This paper presents the first time series clustering benchmark utilizing all time series datasets currently available in the University of California Riverside (UCR) archive — the state of the art repository of time series data. Specifically, the benchmark examines eight popular clustering methods representing three categories of clustering algorithms (partitional, hierarchical and density-based) and three types of distance measures (Euclidean, dynamic time warping, and shape-based), while adhering to six restrictions on datasets and methods to make the comparison as unbiased as possible.
  in Artificial Intelligence AI with algorithms clustering time-series
• How to Develop LSTM Models for Time Series Forecasting
  Long Short-Term Memory networks, or LSTMs for short, can be applied to time series forecasting. There are many types of LSTM models that can be used for each specific type of time series forecasting problem. In this tutorial, you will discover how to develop a suite of LSTM models for a range of standard time series forecasting problems.
  in Artificial Intelligence AI with artificial forecasting intelligence learning lstm machine python rnn time-series

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