Course Description¶
An introduction to time series analysis in the time domain and spectral domain. Topics will include: estimation of trends and seasonal effects, autoregressive moving average models, forecasting, indicators, harmonic analysis, spectra. This course uses Python as its primary computing language.
Instructor and GSIs¶
Liberty Hamilton (Instructor)
Nicholas Liskij (GSI)
Yichen Pan (GSI)
Important Info¶
Class times: Tuesdays and Thursdays, 8-9:30am, VLSB 2060
Lab times: Fridays, 9-11am, 11-1pm Evans 330 and 1-3pm, 3-5pm, Evans 342
Syllabus¶
Basic information about the course can be found in the syllabus (pdf).
Schedule¶
| Jan 20 | Lecture 1 | Introduction to the class | |
| Participation 1 | Pre-semester survey (sign in with berkeley.edu email) | ||
| Jan 22 | Lecture 2 | Characteristics of time series data | |
| Lecture Notes 2 | Lecture 2 notes | ||
| Jan 23 | Lab 1 | Lab 1 | |
| Lab Solutions 1 | Lab 1 Solutions |
| Jan 27 | Lecture 3 | Measures of dependence | |
| Lecture Notes 3 | Lecture 3 notes | ||
| Jan 29 | Lecture 4 | Measures of dependence (part 2) | |
| Lecture Notes 4 | Lecture 4 notes | ||
| Jan 30 | Lab 2 | Lab 2 | |
| Lab Solutions 2 | Lab 2 Solutions |
| Feb 3 | Lecture 5 | Simple linear regression (part 1) | |
| Lecture Notes 5 | Lecture 5 notes | ||
| Lecture Notebook 5 | Lecture 5 Jupyter notebook | ||
| Feb 5 | Lecture 6 | Simple linear regression (part 2) | |
| Lecture Notes 6 | Lecture 6 notes | ||
| Feb 6 | Lab 3 | Lab 3 | |
| Lab Solutions 3 | Lab 3 Solutions |
| Feb 10 | Lecture 7 | Multiple linear regression | |
| Lecture Notes 7 | Lecture 7 notes | ||
| Lecture Notebook 7 | Lecture 7 and 8 notebook - tapping | ||
| Feb 12 | Lecture 8 | Multiple linear regression part 2 | |
| Lecture Notebook 8 (same as 7!) | Lecture 7 and 8 notebook - tapping | ||
| Feb 13 | Lab 4 | Lab 4 | |
| Lab Solutions 4 | Lab 4 Solutions |
| Feb 17 | Lecture 9 | Nonlinear regression | |
| Lecture Notes 9 | Lecture 9 notes | ||
| Feb 19 | Lecture | Class Canceled | |
| Feb 20 | Lab 5 | Lab 5 | |
| Lab Solutions 5 | Lab 5 Solutions |
| Feb 24 | Lecture 10 | Cross-validation and regularization | |
| Lecture Notes 10 | Lecture 10 notes | ||
| Feb 26 | Lecture Notes 11 | Lecture 11 notes | |
| Feb 27 | Lab 6 | Lab 6 | |
| Lab Solutions 6 | Lab 6 Solutions |
| Mar 3 | Lecture 12 | Power Spectral Analysis | |
| Lecture Notes 12 | Lecture 12 notes | ||
| Lecture Notebook 12 | Lecture 12 notebook | ||
| Mar 5 | Lecture 13 | Power Spectral Analysis 2 | |
| Lecture Notes 13 | Lecture 13 notes | ||
| Mar 6 | Lab 7 | Lab 7 | |
| Lab Solutions 7 | Lab 7 Solutions |
| Mar 9 | Lecture 14 | Power Spectral Analysis and Time Frequency Analysis | |
| Lecture Notes 14 | Lecture 14 notes | ||
| Lecture Notebook 14 | Lecture 14 notebook | ||
| Mar 11 | Lecture Notebook 15 | Lecture 15 notebook | |
| Mar 12 | Lab 8 | Exam Review |
| Mar 17 | Exam 1 | Midterm Exam | |
| Mar 19 | Lecture 16 | AR Models | |
| Lecture Notes 16 | Lecture 16 Notes | ||
| Lecture Notebook 16 | Lecture 16 Notebook |
| Mar 31 | Lecture 17 | MA and ARMA Models | |
| Lecture Notes 17 | Lecture 17 Notes | ||
| Apr 2 | Lecture 18 | ARIMA Models | |
| Lecture Notes 18 | Lecture 18 Notes | ||
| Lecture Notebook 18 | Lecture 18 Notebook | ||
| Apr 3 | Lab 9 | Lab 9 |