-
Notifications
You must be signed in to change notification settings - Fork 5
/
Forecasting-1---Introduction.html
231 lines (179 loc) · 7.6 KB
/
Forecasting-1---Introduction.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Forecasting-1---Introduction.utf8</title>
<meta charset="utf-8" />
<link rel="stylesheet" href="my-theme.css" type="text/css" />
</head>
<body>
<textarea id="source">
layout: true
.hheader[<a href="index.html"><svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 576 512"><path d="M280.37 148.26L96 300.11V464a16 16 0 0 0 16 16l112.06-.29a16 16 0 0 0 15.92-16V368a16 16 0 0 1 16-16h64a16 16 0 0 1 16 16v95.64a16 16 0 0 0 16 16.05L464 480a16 16 0 0 0 16-16V300L295.67 148.26a12.19 12.19 0 0 0-15.3 0zM571.6 251.47L488 182.56V44.05a12 12 0 0 0-12-12h-56a12 12 0 0 0-12 12v72.61L318.47 43a48 48 0 0 0-61 0L4.34 251.47a12 12 0 0 0-1.6 16.9l25.5 31A12 12 0 0 0 45.15 301l235.22-193.74a12.19 12.19 0 0 1 15.3 0L530.9 301a12 12 0 0 0 16.9-1.6l25.5-31a12 12 0 0 0-1.7-16.93z"/></svg></a>]
---
class: center, middle, inverse
# Forecasting Time Series
## Eli Holmes
.large[_University of Washington<br>School of Aquatic and Fisheries Sciences<br>Seattle, WA_]
.futnote[Eli Holmes, UW SAFS]
.citation[[email protected]]
---
## Forecasting Time Series
This week we will learn a number of standard approaches for forecasting from time series alone--meaning without any covariates or exogenous variables. At the end of the week, we will address how to incorporate covariates into a time series forecast.
---
## Many approaches are available for non-seasonal modeling
.pull-left.left[
*Stergiou and Christou 1996*
- Time-varying regression
- Box-Jenkins models, aka ARIMA models
- Multivariate time-series approaches
- Harmonic regression
- Dynamic regression
- Vector autoregression (MAR)
- Exponential smoothing (2 variants)
- Exponential surplus yield model (FOX)
]
.pull-right.left[
*Georgakarakos et al. 2006*
- Box-Jenkins models, aka ARIMA models
- Artificial neural networks (ANNs)
- Bayesian dynamic models
*Lawer 2016*
- Box-Jenkins models, aka ARIMA models
- Artificial neural networks (ANNs)
- Exponential Smoothing (6 variants)
]
---
## Forecasting Time Series
We will focus on three of these methods. We will start with time-varying regression as it is simple to apply and understand. The rest of the week we will learn about ARIMA models which have a long tradition and Exponential smoothing models, which are newer and often best performing.
- Time-varying regression
- Box-Jenkins models, aka ARIMA models
- Exponential Smoothing
---
## Stergiou and Christou 1996
I will demonstrate these methods by replicating the work in Stergiou and Christou (1996) Modelling and forecasting annual fisheries catches: comparison of regression, univariate and multivariate time series methods. Fisheries Research 25: 105-136.
---
## Data
We will use the annual landings data from Hellenic (Greek) waters that were used in Stergiou and Christou (1996).
Stergiou and Christou analyzed 16 species. We will look two of the species: Anchovy and Sardine.
Stergiou and Christou used the data from 1964-1989. We have the data up to 2007, but will focus mainly on 1964-1989 (the first half of the time series) to replicate Stergiou and Christou's analyses.
---
---
---
---
Load the data as follows and use only the 1964-1989 data.
```r
load("landings.RData")
landings$log.metric.tons = log(landings$metric.tons)
landings = subset(landings, Year <= 1989)
landings = subset(landings, Species %in% c("Anchovy","Sardine"))
ggplot(landings, aes(x=Year, y=log.metric.tons)) +
geom_line() + facet_wrap(~Species)
```
<img src="Forecasting-1---Introduction_files/figure-html/load_data-1.png" style="display: block; margin: auto;" />
</textarea>
<style data-target="print-only">@media screen {.remark-slide-container{display:block;}.remark-slide-scaler{box-shadow:none;}}</style>
<script src="https://remarkjs.com/downloads/remark-latest.min.js"></script>
<script>var slideshow = remark.create({
"highlightStyle": "github",
"highlightLines": true
});
if (window.HTMLWidgets) slideshow.on('afterShowSlide', function (slide) {
window.dispatchEvent(new Event('resize'));
});
(function(d) {
var s = d.createElement("style"), r = d.querySelector(".remark-slide-scaler");
if (!r) return;
s.type = "text/css"; s.innerHTML = "@page {size: " + r.style.width + " " + r.style.height +"; }";
d.head.appendChild(s);
})(document);
(function(d) {
var el = d.getElementsByClassName("remark-slides-area");
if (!el) return;
var slide, slides = slideshow.getSlides(), els = el[0].children;
for (var i = 1; i < slides.length; i++) {
slide = slides[i];
if (slide.properties.continued === "true" || slide.properties.count === "false") {
els[i - 1].className += ' has-continuation';
}
}
var s = d.createElement("style");
s.type = "text/css"; s.innerHTML = "@media print { .has-continuation { display: none; } }";
d.head.appendChild(s);
})(document);
// delete the temporary CSS (for displaying all slides initially) when the user
// starts to view slides
(function() {
var deleted = false;
slideshow.on('beforeShowSlide', function(slide) {
if (deleted) return;
var sheets = document.styleSheets, node;
for (var i = 0; i < sheets.length; i++) {
node = sheets[i].ownerNode;
if (node.dataset["target"] !== "print-only") continue;
node.parentNode.removeChild(node);
}
deleted = true;
});
})();
// adds .remark-code-has-line-highlighted class to <pre> parent elements
// of code chunks containing highlighted lines with class .remark-code-line-highlighted
(function(d) {
const hlines = d.querySelectorAll('.remark-code-line-highlighted');
const preParents = [];
const findPreParent = function(line, p = 0) {
if (p > 1) return null; // traverse up no further than grandparent
const el = line.parentElement;
return el.tagName === "PRE" ? el : findPreParent(el, ++p);
};
for (let line of hlines) {
let pre = findPreParent(line);
if (pre && !preParents.includes(pre)) preParents.push(pre);
}
preParents.forEach(p => p.classList.add("remark-code-has-line-highlighted"));
})(document);</script>
<script>
(function() {
var links = document.getElementsByTagName('a');
for (var i = 0; i < links.length; i++) {
if (/^(https?:)?\/\//.test(links[i].getAttribute('href'))) {
links[i].target = '_blank';
}
}
})();
</script>
<script>
slideshow._releaseMath = function(el) {
var i, text, code, codes = el.getElementsByTagName('code');
for (i = 0; i < codes.length;) {
code = codes[i];
if (code.parentNode.tagName !== 'PRE' && code.childElementCount === 0) {
text = code.textContent;
if (/^\\\((.|\s)+\\\)$/.test(text) || /^\\\[(.|\s)+\\\]$/.test(text) ||
/^\$\$(.|\s)+\$\$$/.test(text) ||
/^\\begin\{([^}]+)\}(.|\s)+\\end\{[^}]+\}$/.test(text)) {
code.outerHTML = code.innerHTML; // remove <code></code>
continue;
}
}
i++;
}
};
slideshow._releaseMath(document);
</script>
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement('script');
script.type = 'text/javascript';
script.src = 'https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-MML-AM_CHTML';
if (location.protocol !== 'file:' && /^https?:/.test(script.src))
script.src = script.src.replace(/^https?:/, '');
document.getElementsByTagName('head')[0].appendChild(script);
})();
</script>
</body>
</html>