-
Notifications
You must be signed in to change notification settings - Fork 101
/
_pkgdown.yml
244 lines (243 loc) · 7.28 KB
/
_pkgdown.yml
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
232
233
234
235
236
237
238
239
240
241
242
243
url: https://business-science.github.io/timetk/
template:
bootstrap: 5
bootswatch: lux
params:
ganalytics: G-20GDZ5LL77
navbar:
bg: primary
title: timetk
left:
- icon: fa-home
href: index.html
- text: Start
href: articles/TK04_Plotting_Time_Series.html
- text: Articles
href: articles/index.html
menu:
- text: Visualization
- text: Plotting Time Series
href: articles/TK04_Plotting_Time_Series.html
- text: Plotting Seasonality and Correlation
href: articles/TK05_Plotting_Seasonality_and_Correlation.html
- text: '---'
- text: Data Wrangling
- text: Time Series Data Wrangling
href: articles/TK07_Time_Series_Data_Wrangling.html
- text: '---'
- text: Machine Learning
- text: Time Series Machine Learning
href: articles/TK03_Forecasting_Using_Time_Series_Signature.html
- text: Anomaly Detection
href: articles/TK08_Automatic_Anomaly_Detection.html
- text: Clustering
href: articles/TK09_Clustering.html
- text: '---'
- text: Time Series Fundamentals
- text: Calendar Features
href: articles/TK01_Working_With_Time_Series_Index.html
- text: Frequency and Trend
href: articles/TK06_Automatic_Frequency_And_Trend_Selection.html
- text: '---'
- text: Time Sequences & Data Structures
- text: Intelligent Date & Time Sequences
href: articles/TK02_Time_Series_Date_Sequences.html
- text: Time Series Class Conversion (tbl, ts, zoo, & xts)
href: articles/TK00_Time_Series_Coercion.html
- text: API
href: reference/index.html
menu:
- text: API Functions
- icon: fa-home
text: Function Reference
href: reference/index.html
- text: '---'
- text: Change History
- text: News
href: news/index.html
- text: R Ecosystem
menu:
- text: Forecast
- text: Modeltime (Forecasting)
href: https://business-science.github.io/modeltime/
- text: TimeTK (Time Series Analysis)
href: https://business-science.github.io/timetk/
- text: '---'
- text: Improve
- text: Modeltime Ensemble (Blending Forecasts)
href: https://business-science.github.io/modeltime.ensemble/
- text: Modeltime Resample (Backtesting)
href: https://business-science.github.io/modeltime.resample/
- text: '---'
- text: Scale
- text: Modeltime H2O (AutoML)
href: https://business-science.github.io/modeltime.h2o/
- text: Modeltime GluonTS (Deep Learning)
href: https://business-science.github.io/modeltime.gluonts/
- text: Python
menu:
- text: Forecast
- text: Timetk for Python (Time Series Analysis)
href: https://business-science.github.io/pytimetk/
- icon: fas fa-graduation-cap
text: Learn
href: https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting/
right:
- icon: fa-github
href: https://github.com/business-science/timetk
reference:
- title: Plotting Time Series
desc: __Detect relationships through visualizations__
- subtitle: Time Series Plotting
contents:
- plot_time_series
- plot_time_series_boxplot
- plot_time_series_regression
- subtitle: Correlation, Seasonalilty, & Anomaly Plotting
contents:
- contains("plot_acf")
- contains("plot_anomaly")
- contains("plot_seasonal")
- contains("plot_stl")
- title: Time Series Data Wrangling Operations
desc: __Extension for `dplyr` for time-series data manipulations__
- subtitle: Data Frame Operations
contents:
- contains("summarise")
- contains("mutate")
- pad_by_time
- filter_by_time
- filter_period
- slice_period
- condense_period
- future_frame
- subtitle: Anomaly Detection
contents:
- anomalize
- contains("plot_anomalies")
- subtitle: Function Operations
contents: slidify
- subtitle: Vector Operations
contents:
- between_time
- add_time
- title: Time Series Features
desc: Tidy integration with `tsfeatures`
contents: tk_tsfeatures
- title: Augment Operations (Quickly Add Many Features)
desc: __Add multiple columns to the original data. Respects `dplyr` groups.__
contents:
- tk_augment_timeseries_signature
- tk_augment_holiday_signature
- tk_augment_slidify
- tk_augment_differences
- tk_augment_lags
- starts_with("tk_augment")
- title: Vectorized Transformations
desc: __Use with `mutate` to apply vectorized transformations to time series data__
contents:
- contains("box_cox_vec")
- contains("diff_vec")
- contains("lag_vec")
- standardize_vec
- normalize_vec
- contains("_vec")
- title: Feature Engineering Operations (Recipe Steps)
desc: __Preprocessing & feature engineering operations for use with `recipes` and
the `tidymodels` ecosystem__
- subtitle: Engineered Features
- contents:
- step_timeseries_signature
- step_holiday_signature
- step_fourier
- subtitle: Lags & Diffs
desc: See `recipes::step_lag()` for lagged features.
- contents: step_diff
- subtitle: Smoothing & Rolling
- contents:
- step_smooth
- step_slidify
- step_slidify_augment
- subtitle: Variance Reduction
desc: See `recipes::step_log()` for log transformation.
- contents:
- step_box_cox
- step_log_interval
- subtitle: Add Rows to a Time series
contents: step_ts_pad
- subtitle: Imputation & Outlier Cleaning
desc: See `recipes::step_rollimpute()` for rolling imputation.
- contents:
- step_ts_impute
- step_ts_clean
- title: Cross Validation Operations (Rsample & Tune)
desc: __Resampling for time series cross validation using `rsamples`__
- subtitle: Time Series Cross Validation (Resample Sets)
contents:
- time_series_split
- time_series_cv
- subtitle: Cross Validation Plan Visualization (Resample Sets)
desc: Uses the output of `time_series_cv` or `rsample::rolling_origin`
contents:
- plot_time_series_cv_plan
- tk_time_series_cv_plan
- title: Index Operations
desc: __Extract and check the date or date-time index.__
contents:
- starts_with("tk_index")
- starts_with("has_timetk_idx")
- title: Make Operations
desc: __Make time series sequences.__
contents:
- tk_make_timeseries
- starts_with("tk_make_future")
- starts_with("tk_make")
- title: Get Operations
desc: __Get summaries, frequency, and signatures from the time series index.__
contents:
- tk_get_timeseries_signature
- tk_get_holiday_signature
- contains("tk_get")
- title: Diagnostic Operations
desc: __These power the time series plotting functions__
contents:
- tk_summary_diagnostics
- tk_anomaly_diagnostics
- tk_acf_diagnostics
- tk_seasonal_diagnostics
- tk_stl_diagnostics
- title: Conversion Operations
desc: __Functions for converting between common time series formats.__
contents:
- tk_tbl
- tk_ts
- tk_ts_
- tk_xts
- tk_xts_
- tk_zoo
- tk_zoo_
- tk_zooreg
- tk_zooreg_
- title: Time Scale Template
desc: __The timescale template is used to automate frequency and trendcycle calculations.__
contents: contains("time_scale")
- title: Time Series Datasets
desc: __Time series from various forecasting competitions. Domains include economic,
retail, and web (google analytics)__
contents:
- m4_hourly
- m4_daily
- m4_weekly
- contains("m4_")
- contains("walmart")
- contains("wikipedia")
- bike_sharing_daily
- taylor_30_min
- FANG
- title: Date Utilities
contents:
- parse_date2
- is_date_class
- title: Package Information
contents:
- timetk