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

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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).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.

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### Exploratory time series data analysis

**Free**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.

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

Prerequisites

Intermediate RAssociate 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.

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Lloyds Banking Group

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Harvard Business School

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Decision Science Analytics, USAA

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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).