Time Series

April 29, 2020

Time Series Feature Extraction for industrial big data (IIoT) applications

Feature Extraction by Distributed and Parallel means for industrial big data applications Motivation — Why Feature extraction is necessary? Feature extraction remains one of the most preliminary steps in machine learning algorithms to identify strong and weak relevant attributes. While many feature extraction algorithms are used during Feature Engineering for standard classification and regression problems, the problem turns increasing difficult for time series classification and regression problems where each label or regression target is associated […]

ARIMA/SARIMA vs LSTM with Ensemble learning Insights for Time Series Data

An Insight of Time Series Traditional vs Deep Learning Technique Motivation There are most commonly five types of traditional time series models, which includes the following. They have played an important role in different industry for time-series forecasting. Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. AR models have been known to express the current value of the time series linearly. […]