Time Series Analysis in R
Learn the core techniques necessary to extract meaningful insights from time series data.
Start Course for Free4 Horas16 Videos58 Exercises
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.Training 2 or more people?Try DataCamp For Business
Loved by learners at thousands of companies
Descrição do Curso
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.
For Business
Training 2 or more people?
Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and moreNas seguintes faixas
Analista quantitativo com R
Ir para a trilhaSéries temporais com R
Ir para a trilha- 1
Exploratory time series data analysis
LivreThis 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.
Welcome to the course!50 xpExploring raw time series100 xpBasic time series plots100 xpWhat does the time index tell us?100 xpSampling frequency50 xpIdentifying the sampling frequency100 xpWhen is the sampling frequency exact?50 xpMissing values100 xpBasic time series objects50 xpCreating a time series object with ts()100 xpTesting whether an object is a time series100 xpPlotting a time series object100 xp - 2
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.
Trend spotting!50 xpRandom or not random?50 xpName that trend50 xpRemoving trends in variability via the logarithmic transformation100 xpRemoving trends in level by differencing100 xpRemoving seasonal trends with seasonal differencing100 xpThe white noise (WN) model50 xpSimulate the white noise model100 xpEstimate the white noise model100 xpThe random walk (RW) model50 xpSimulate the random walk model100 xpSimulate the random walk model with a drift100 xpEstimate the random walk model100 xpStationary processes50 xpStationary or not?50 xpAre the white noise model or the random walk model stationary?100 xp - 3
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.
Scatterplots50 xpAsset prices vs. asset returns100 xpCharacteristics of financial time series100 xpPlotting pairs of data100 xpCovariance and correlation50 xpCalculating sample covariances and correlations100 xpGuess the correlation coefficient50 xpAutocorrelation50 xpCalculating autocorrelations100 xpThe autocorrelation function100 xpVisualizing the autocorrelation function100 xp - 4
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.
The autoregressive model50 xpSimulate the autoregressive model100 xpEstimate the autocorrelation function (ACF) for an autoregression100 xpPersistence and anti-persistence50 xpCompare the random walk (RW) and autoregressive (AR) models100 xpAR model estimation and forecasting50 xpEstimate the autoregressive (AR) model100 xpSimple forecasts from an estimated AR model100 xp - 5
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.
The simple moving average model50 xpSimulate the simple moving average model100 xpEstimate the autocorrelation function (ACF) for a moving average100 xpMA model estimation and forecasting50 xpEstimate the simple moving average model100 xpSimple forecasts from an estimated MA model100 xpCompare AR and MA models50 xpAR vs MA models100 xpName that model by time series plot50 xpName that model by ACF plot50 xpCongratulations!50 xp
For Business
Training 2 or more people?
Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and moreNas seguintes faixas
Analista quantitativo com R
Ir para a trilhaSéries temporais com R
Ir para a trilhaCollaborators
Prerequisites
Intermediate RDavid S. Matteson
Veja Mais
Associate Professor at Cornell University
What do other learners have to say?
Join over 13 million learners and start Time Series Analysis in R today!
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.