Course Introduction to Time Series Analysis

Learn the core techniques necessary to extract meaningful insights from time series data.

DescriptionChaptersExercisesInstructor

This online course about Introduction to Time Series Analysis covers a key part of what a future data analyst would require.

Many phenomena in our day-to-day lives, such as the movement of stock prices, are measured in intervals over a period of time. Time series analysis methods are extremely useful for analyzing these special data types. In this course, you will be introduced to some core time series analysis concepts and techniques.

Enroll now in this Introduction to Time Series Analysis course, and don’t miss the opportunity of learning with the best, as David S. Matteson is. With 58 enriching exercises, 16 videos, and an estimated time of 4 hours to successfully end up the course, you will become one of the best.

Requisites before you start
Chapter 1: Exploratory time series data analysis
This chapter will give you insights on how to organize and visualize time series data in R. You will learn several simplifying assumptions that are widely used in time series analysis, and common characteristics of financial time series.
Chapter 2: Correlation analysis and the autocorrelation function
In this chapter, you will review the correlation coefficient, use it to compare two time series, and also apply it to compare a time series with its past, as an autocorrelation. You will discover the autocorrelation function (ACF) and practice estimating and visualizing autocorrelations for time series data.
Chapter 3: A simple moving average
In this chapter, you will learn the simple moving average (MA) model and several of its basic properties. You will also practice simulating and estimating the MA model in R, and compare the MA model with the autoregressive (AR) model.
Chapter 4: Predicting the future
In this chapter, you will conduct some trend spotting, and learn the white noise (WN) model, the random walk (RW) model, and the definition of stationary processes.
Chapter 5: Autoregression
In this chapter, you will learn the autoregressive (AR) model and several of its basic properties. You will also practice simulating and estimating the AR model in R, and compare the AR model with the random walk (RW) model.
Chapter 6: Exploratory time series data analysis
This chapter will give you insights on how to organize and visualize time series data in R. You will learn several simplifying assumptions that are widely used in time series analysis, and common characteristics of financial time series.
Chapter 7: Predicting the future
In this chapter, you will conduct some trend spotting, and learn the white noise (WN) model, the random walk (RW) model, and the definition of stationary processes.
Chapter 8: Correlation analysis and the autocorrelation function
In this chapter, you will review the correlation coefficient, use it to compare two time series, and also apply it to compare a time series with its past, as an autocorrelation. You will discover the autocorrelation function (ACF) and practice estimating and visualizing autocorrelations for time series data.
Chapter 9: Autoregression
In this chapter, you will learn the autoregressive (AR) model and several of its basic properties. You will also practice simulating and estimating the AR model in R, and compare the AR model with the random walk (RW) model.
Chapter 10: A simple moving average
In this chapter, you will learn the simple moving average (MA) model and several of its basic properties. You will also practice simulating and estimating the MA model in R, and compare the MA model with the autoregressive (AR) model.
Introduction to Time Series Analysis. Learn the core techniques necessary to extract meaningful insights from time series data.

David S. Matteson

Associate Professor at Cornell University

David S. Matteson is Professor of Statistical Science at Cornell University and co-author of Statistics and Data Analysis for Financial Engineering with R examples.

Collaborators

#R #Python #MachineLearning #BigData #DataAnalysis