Ch. 1 - Exploratory time series data analysis
Welcome to the course!
[Video]
Exploring raw time series
Basic time series plots
What does the time index tell us?
Sampling frequency
Identifying the sampling frequency
When is the sampling frequency exact?
Missing values
Basic time series objects
Creating a time series object with ts()
Testing whether an object is a time series
Plotting a time series object
Ch. 2 - Predicting the future
Trend spotting!
Random or not random?
Name that trend
Removing trends in variability via the logarithmic transformation
Removing trends in level by differencing
Removing seasonal trends with seasonal differencing
The white noise (WN) model
Simulate the white noise model
Estimate the white noise model
The random walk (RW) model
Simulate the random walk model
Simulate the random walk model with a drift
Estimate the random walk model
Stationary processes
Stationary or not?
Are the white noise model or the random walk model stationary?
Ch. 3 - Correlation analysis and the autocorrelation function
Scatterplots
Asset prices vs. asset returns
Characteristics of financial time series
Plotting pairs of data
Covariance and correlation
Calculating sample covariances and correlations
Guess the correlation coefficient
Autocorrelation
Calculating autocorrelations
The autocorrelation function
Visualizing the autocorrelation function
Ch. 4 - Autoregression
The autoregressive model
Simulate the autoregressive model
Estimate the autocorrelation function (ACF) for an autoregression
Persistence and anti-persistence
Compare the random walk (RW) and autoregressive (AR) models
AR model estimation and forecasting
Estimate the autoregressive (AR) model
Simple forecasts from an estimated AR model
Ch. 5 - A simple moving average
The simple moving average model
Simulate the simple moving average model
Estimate the autocorrelation function (ACF) for a moving average
MA model estimation and forecasting
Estimate the simple moving average model
Simple forecasts from an estimated MA model
Compare AR and MA models
AR vs MA models
Name that model by time series plot
Name that model by ACF plot
Congratulations!
About Michael Mallari
Michael is a hybrid thinker and doer—a byproduct of being a CliftonStrengths “Learner” over time. With 20+ years of engineering, design, and product experience, he helps organizations identify market needs, mobilize internal and external resources, and deliver delightful digital customer experiences that align with business goals. He has been entrusted with problem-solving for brands—ranging from Fortune 500 companies to early-stage startups to not-for-profit organizations.
Michael earned his BS in Computer Science from New York Institute of Technology and his MBA from the University of Maryland, College Park. He is also a candidate to receive his MS in Applied Analytics from Columbia University.
LinkedIn | Twitter | www.michaelmallari.com/data | www.columbia.edu/~mm5470