Time Series Analysis: Lecture Notes with Examples in R
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| 3 (Sin recargo) | Q118.33 |
| 6 | Q62.42 |
| 10 | Q37.63 |
| 12 | Q31.65 |
| 15 | Q25.68 |
| 18 | Q21.79 |
| 24 | Q16.64 |
| 36 | Q11.14 |
| 48 | Q8.39 |
| Cuotas | Monto |
| 3 (Sin recargo) | Q118.33 |
| 6 (Sin recargo) | Q59.17 |
| 10 (Sin recargo) | Q35.50 |
| 12 (Sin recargo) | Q29.58 |
| 15 | Q25.09 |
| 18 | Q21.10 |
| 24 | Q16.27 |
| 36 | Q11.24 |
| 48 | Q8.28 |
| Cuotas | Monto |
| 3 (Sin recargo) | Q118.33 |
| 6 | Q62.42 |
| 10 | Q37.63 |
| 12 | Q31.65 |
| 15 | Q25.68 |
| 18 | Q21.79 |
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- This is a collection of lecture notes on applied time series analysis and forecasting using the statistical programming language R. Many of these lectures are based on the original notes by Y. R. Gel and C. Cutler for the course STAT-443 Forecasting (University of Waterloo, Canada) adapted and expanded by V. Lyubchich for the course MEES-713 Environmental Statistics 2 (University of Maryland, USA). Each lecture starts by listing the learning objectives and required reading materials, with additional references in the text. The notes introduce the methods and give a few examples but are less detailed than the reading materials. The notes do not substitute a textbook. The audience is expected to be familiar with R programming and the following statistical concepts and methods: probability distributions, sampling inference and hypothesis testing, correlation analysis, and regression analysis (including simple and multiple linear regression, mixed-effects models, generalized linear models, and generalized additive models).
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