Author Aileen Nielsen provides practical guidance working with time series data for the purposes of insights. Learn how statistics and machine learning offer new advantages for time series analysis through real-world use cases.
A Historical Perspective of Time Series Data
When time series analysis became interesting and how statistics and machine learning have followed the data
Storing Temporal Data
Describes the requirements for analyzing time series data, comparing and contrasting SQL vs NoSQL solutions along with popular database solutions
IoT and Transaction Data
Learn how to extract actionable insights from existing transaction records and new Internet of Things (IoT) data
Time Series Use Cases
Customer interactions, machine behavior, and fraudulent activities can be tracked and responded to using time series data
Gain Knowledge of Statistical Techniques
See methods of dealing with autocorrelation and non-stationary time series. Learn autoregressive models such as ARMA and ARIMA
Machine Learning for Predictive Models
Discover recent advancements in deep learning to build accurate forecasting models for time series
About the Author
Aileen Nielsen is a software engineer at an early-stage NYC startup that leverages time series data and neural networks. Previously, Aileen has worked in corporate law, physics research laboratories, and, most recently, NYC startups oriented towards healthcare.
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