Eikosstatistics & data science

Time Series, Health & Evidence Synthesis · Time Series & Forecasting

Time series: non-stationary and seasonal processes — advanced theory

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Introduction Econometrics — Advanced Time Series

Overview

Topics covered

  • Introduction
  • Non-Stationary and Seasonal Processes
  • The Stationarity Paradigm and its Breakdown
  • Notation and Glossary of Operators
  • Types of Non-Stationarity in the Mean
  • Trend-Stationary Models vs. Difference-Stationary

Related concepts

Main connections

  • Time series & ARIMA
  • Hypothesis testing
  • Variance & standard deviation
  • Residual analysis & diagnostics
  • Mean & expected value
  • Mathematical expectation & moments

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

FYt1,Yt2,…,Ytk(y1,y2,…,yk)=FYt1+τ,Yt2+τ,…,Ytk+τ(y1,y2,…,yk)
View LaTeX sourceF_{Y_{t_1}, Y_{t_2}, \dots, Y_{t_k}}(y_1, y_2, \dots, y_k) = F_{Y_{t_1+\tau}, Y_{t_2+\tau}, \dots, Y_{t_k+\tau}}(y_1, y_2, \dots, y_k)