951
## wday.lbl^6 2.276e+01 8.200e+01 0.278 0.782319
## mday NA NA NA NA
## qday -1.362e+02 2.418e+02 -0.563 0.575326
## yday -2.356e+02 1.416e+02 -1.664 0.101627
## mweek -1.670e+02 1.477e+02 -1.131 0.262923
## week -1.764e+02 1.890e+02 -0.933 0.354618
## week.iso 2.315e+02 1.256e+02 1.842 0.070613 .
## week2 -1.235e+02 1.547e+02 -0.798 0.428283
## week3 NA NA NA NA
## week4 NA NA NA NA
## mday7 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 260.4 on 57 degrees of freedom
## Multiple R-squared: 0.9798, Adjusted R-squared: 0.9706
## F-statistic: 106.4 on 26 and 57 DF, p-value: < 2.2e-16
STEP 3：构建未来（新）数据
使用 tk_index() 来扩展索引。
# Retrieves the timestamp information
beer_sales_idx <- beer_sales_tbl %>%
tk_index()
tail(beer_sales_idx)
## [1] "2016-07-01" "2016-08-01" "2016-09-01" "2016-10-01" "2016-11-01"
## [6] "2016-12-01"
通过 tk_make_future_timeseries 函数从现有索引构造未来索引。函数会在内部检查索引的周期性，并返回正确的序列。我们在 whole vignette on how to make future time series 介绍了该主题更详尽的内容。
# Make future index
future_idx <- beer_sales_idx %>%
tk_make_future_timeseries(
n_future = 12)
future_idx
## [1] "2017-01-01" "2017-02-01" "2017-03-01" "2017-04-01" "2017-05-01"
## [6] "2017-06-01" "2017-07-01" "2017-08-01" "2017-09-01" "2017-10-01"
## [11] "2017-11-01" "2017-12-01"
用 tk_get_timeseries_signature() 将未来索引转换成时间序列签名数据框。
new_data_tbl <- future_idx %>%
tk_get_timeseries_signature()
new_data_tbl
## # A tibble: 12 x 29
## index index.num diff year year.iso half quarter month
## <date> <int> <int> <int> <int> <int> <int> <int>
## 1 2017-01-01 1483228800 NA 2017 2016 1 1 1
## 2 2017-02-01 1485907200 2678400 2017 2017 1 1 2
## 3 2017-03-01 1488326400 2419200 2017 2017 1 1 3
## 4 2017-04-01 1491004800 2678400 2017 2017 1 2 4
## 5 2017-05-01 1493596800 2592000 2017 2017 1 2 5
## 6 2017-06-01 1496275200 2678400 2017 2017 1 2 6
## 7 2017-07-01 1498867200 2592000 2017 2017 2 3 7
## 8 2017-08-01 1501545600 2678400 2017 2017 2 3 8
## 9 2017-09-01 1504224000 2678400 2017 2017 2 3 9
## 10 2017-10-01 1506816000 2592000 2017 2017 2 4 10
## 11 2017-11-01 1509494400 2678400 2017 2017 2 4 11
## 12 2017-12-01 1512086400 2592000 2017 2017 2 4 12
## # ... with 21 more variables: month.xts <int>, month.lbl <ord>,
## # day <int>, hour <int>, minute <int>, second <int>, hour12 <int>,
## # am.pm <int>, wday <int>, wday.xts <int>, wday.lbl <ord>,
## # mday <int>, qday <int>, yday <int>, mweek <int>, week <int>,
## # week.iso <int>, week2 <int>, week3 <int>, week4 <int>,
## # mday7 <int>
STEP 4：预测新数据
将 predict() 应用于回归模型。注意，和之前使用 lm() 函数时一样，去掉 index 和 diff 列。
# Make predictions
pred <- predict(
fit_lm,
newdata = select(
new_data_tbl, -c(index, diff)))
predictions_tbl <- tibble(
date = future_idx,
value = pred)
predictions_tbl
## # A tibble: 12 x 2
## date value
## <date> <dbl>
## 1 2017-01-01 9509.122
## 2 2017-02-01 10909.189
## 3 2017-03-01 12281.835
## 4 2017-04-01 11378.678
## 5 2017-05-01 13080.710
## 6 2017-06-01 13583.471
## 7 2017-07-0