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Cutting Edge Approaches to Time Series Analysis

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What You Will Learn From This Report

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.
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A Historical Perspective of Time Series Data
When time series analysis became interesting and how statistics and machine learning have followed the data
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Storing Temporal Data
Describes the requirements for analyzing time series data, comparing and contrasting SQL vs NoSQL solutions along with popular database solutions
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IoT and Transaction Data
Learn how to extract actionable insights from existing transaction records and new Internet of Things (IoT) data
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Time Series Use Cases
Customer interactions, machine behavior, and fraudulent activities can be tracked and responded to using time series data
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Gain Knowledge of Statistical Techniques
See methods of dealing with autocorrelation and non-stationary time series. Learn autoregressive models such as ARMA and ARIMA
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Machine Learning for Predictive Models
Discover recent advancements in deep learning to build accurate forecasting models for time series

About the Author

Aileen Nielsen

Aileen Nielsen

Software Engineer
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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Learn how statistics and machine learning offer new advantages for time series analysis through real-world use cases.
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