Mohamed Ibrahim, Ph.D., Sr. Engineering Consultant at Advansys ESC. Mohamed holds a Ph.D. in computer Engineering from Kyutech, Japan. He worked at the architecture design center of Hitachi Ltd., Japan, where he participated in designing predictive analytics solutions for Smart Data Centers (SDC) and modelling of storage arrays performance. He designed and qualified a smart multiprocessor system on-chip for LEO satellites at Kyutech. His research interests are in designing high performance systolic arrays for machine learning, fault tolerant computers, high performance embedded computers and reconfigurable systems-on-chip.
About the Tutorial
Time Series Analysis Using Python
Goals and objectives
Time series is a collection of data over time periods. It could be data collected from sensors, econometrics, or even sales revenues. Analyzing such serieses helps in building insights from finding correlations among variables and forecasting future values. In this tutorial we will explore how to use Python in performing time series analysis. It would cover topics of multivariate analysis, handling missing values, time series decomposition, data visualization, cross and auto correlation, ARIMA forecasting model, RNN and LSTM forecasting models.
Basic knowledge in statistics and Python