Autoregressive Models in TensorFlow – Towards Data Science

A time series can have different properties depending on the generating process and how the process is measured. There are many properties that describe a time series: 1) stationary 2) continuous 3) random 4) periodic. I'll focus on non-random signals for this post, but I would recommend Applied Stochastic Processes, Chaos Modeling, and Probabilistic Properties of Numeration Systems By Vincent Granville, Ph.D. to anyone interested in random processes. Most data scientist working with B2B or B2C time series are primarily working with non-continuous, or discrete, processes. Discrete means that the data are collected at fixed time intervals.