Practical Time Series Analysis

Index

A

• abstract data type, Building a Simulation Universe That Runs Itself
• accuracy, performance versus, Performance Considerations in Fitting and Serving Time Series Models
• ACF (see autocorrelation function)
• actuarial tables, Medicine as a Time Series Problem
• additive regression model, Facebook’s Open Source Prophet Package
• agent-based modeling, Building a Simulation Universe That Runs Itself
• aggregation
  • Google's in-house forecasting framework, Temporal aggregation and geographic/conceptual disaggregation
  • of government data, Upsample and Aggregate the Data as We Iterate Through It
• AirPassengers data set, Intuition, The partial autocorrelation function
  • 2D visualizations, 2D Visualizations-2D Visualizations
  • 3D visualizations, 3D Visualizations-3D Visualizations
  • smoothing data, Exponential smoothing
• Akaike information criterion (AIC), Choosing parameters for an AR(p) model
• alpha parameter (smoothing factor), Exponential smoothing
• AlphaVantage, Obtaining and Exploring Financial Data
• Amazon time series prediction service, More Resources, Forecasting as a Service
• AnomalyDetection (Twitter package), Twitter’s Open Source AnomalyDetection Package-Twitter’s Open Source AnomalyDetection Package
• AR(p) model, Using algebra to understand constraints on AR processes
  • choosing parameters for, Choosing parameters for an AR(p) model-Choosing parameters for an AR(p) model
  • forecasting with, Forecasting with an AR(p) process-Forecasting many steps into the future
  • as moving window function, Forecasting one time step ahead ...