  This paper proposes nowcasting of high-frequency financial datasets in
real-time with a 5-minute interval using the streaming analytics feature of
Apache Spark. The proposed 2 stage method consists of modelling chaos in the
first stage and then using a sliding window approach for training with machine
learning algorithms namely Lasso Regression, Ridge Regression, Generalised
Linear Model, Gradient Boosting Tree and Random Forest available in the MLLib
of Apache Spark in the second stage. For testing the effectiveness of the
proposed methodology, 3 different datasets, of which two are stock markets
namely National Stock Exchange & Bombay Stock Exchange, and finally One
Bitcoin-INR conversion dataset. For evaluating the proposed methodology, we
used metrics such as Symmetric Mean Absolute Percentage Error, Directional
Symmetry, and Theil U Coefficient. We tested the significance of each pair of
models using the Diebold Mariano (DM) test.

Machine Learning;Computational Engineering, Finance, and Science