Analyzing Time Series and Sequential Data Specialization

large-scale-forecasting-sas-viya

specialization-overview-review

welcome
1 overview
2 getting-the-most-from-this-specialization_instructions
3 using-forum-and-getting-help_instructions
4 frequently-asked-questions_instructions

course-overview

course-overview
5 welcome-to-the-course
6 prerequisites_instructions
7 accessing-the-course-files-and-practicing-in-this-course-required_instructions

introduction-to-large-scale-forecasting

module-overview
8 about-this-module

the-large-scale-forecasting-problem
9 large-scale-forecasting
10 analysts-and-algorithms

forecasting-system-overview
11 atsm-package-objects
12 objects-and-information-flows
13 other-useful-configurations

exploring-and-processing-timestamped-data

module-overview
14 about-this-module

accumulation-transforming-transactional-data-into-time-series-data
15 time-series-accumulation
16 time-binning-and-indexing
17 accumulation-in-the-tsmodel-procedure
18 demo-accumulation-using-the-tsmodel-procedure

handling-missing-and-zero-valued-intervals
19 missing-value-interpretation
20 missing-value-imputation
21 demo-missing-value-interpretation-and-imputation

aggregation-building-the-data-hierarchy
22 time-series-aggregation
23 building-the-data-hierarchy-in-tsmodel
24 demo-using-proc-tsmodel-to-create-the-data-hierarchy

packages-for-tsmodel
25 proc-tsmodel-packages
26 using-proc-tsmodel-packages

automatic-forecasting-model-specification-and-selection

module-overview
27 about-this-module
28 introduction-to-atsm-objects

automatic-model-specification
29 the-diagspec-object
30 diagspec-object-methods
31 the-diagnose-object

automatic-forecast-generation
32 the-foreng-object
33 collector-objects
34 demo-automatic-model-selection-using-the-atsm-package

creating-custom-models-and-managing-model-lists

module-overview
35 about-this-module
36 custom-models-and-the-tsm-package

the-time-series-model-tsm-package
37 the-tsm-package
38 tsm-package-syntax-highlights
39 demo-creating-and-fitting-a-custom-specification-with-the-tsm-package

adding-custom-models-to-the-automatic-forecasting-system
40 adding-custom-models
41 demo-combining-custom-and-system-generated-models-in-the-model-selection-process

event-variables-in-the-forecasting-system

module-overview
42 about-this-module

introduction-to-event-variables
43 introduction-to-event-variables
44 event-variables-in-sas-visual-forecasting

creating-event-variables-in-the-atsm-package
45 creating-event-variables-in-the-atsm-package
46 implementing-event-variables-defined-in-the-atsm-package
47 demo-creating-and-implementing-event-variables-in-the-atsm-package

creating-event-variables-with-the-hpfevents-procedure
48 creating-event-variables-in-the-hpfevents-procedure
49 implementing-event-variables-defined-in-the-hpfevents-procedure
50 demo-creating-event-variables-in-the-hpfevents-procedure-and-implementing-them

by-group-processing-for-event-variables
51 by-group-functionality
52 implementing-by-group-processing-for-event-variables
53 demo-by-group-processing-for-event-variables

reconciling-statistical-forecasts

module-overview-and-introduction
54 about-this-module
55 reconciliation-basics

basic-forecast-reconciliation
56 performing-basic-forecast-reconciliation
57 demo-top-down-reconciliation-using-the-tsreconcile-procedure

disaggregation-methods
58 performing-disaggregation

bottom-up-reconciliation
59 performing-bottom-up-reconciliation
60 demo-performing-bottom-up-reconciliation

setting-up-the-forecasting-system-and-generating-best-forecasts

module-overview
61 about-this-module

honest-assessment-and-baseline-performance
62 holdout-sample-model-selection
63 holdout-partitioning
64 performance-measures
65 demo-implementing-honest-assessment-for-model-selection-and-creating-benchmark

combined-model-forecasts
66 combined-models
67 demo-adding-combined-models-to-the-forecasting-system

outlier-detection
68 outlier-detection
69 demo-adding-outlier-detection-to-the-forecasting-system

conditional-processing-and-error-catching
70 conditional-processing
71 demo-conditional-processing-and-error-catching

rolling-the-forecasting-system-forward-in-time
72 rolling-the-forecasting-system-forward-in-time
73 stability-and-updating-models
74 demo-rolling-the-system-forward-in-time

modeling-time-series-and-sequential-data

specialization-overview-review

welcome
75 overview
76 getting-the-most-from-this-specialization_instructions
77 using-forum-and-getting-help_instructions

course-overview

course-overview-and-logistics
78 welcome-to-the-course
79 prerequisites_instructions
80 finding-the-course-files-and-practicing-in-this-course-required_instructions
81 frequently-asked-questions_instructions

introduction-to-time-series

module-overview
82 about-this-module

a-review-of-time-series-components-and-concepts
83 time-series-components
84 applications-of-time-series-analysis
85 demo-exploring-a-time-series
86 a-framework-for-forecasting
87 demo-accumulating-a-time-series-and-exploring-systematic-variation

simple-models-for-time-series
88 concepts-and-notation
89 naive-models

exponential-smoothing-models
90 introduction-to-exponential-smoothing-models-esm
91 esm-and-signal-components
92 demo-forecasting-with-esm

arimax-models

module-overview
93 about-this-module

arma-models
94 models-for-stationary-data
95 autoregressive-moving-average-models
96 identifying-arma-models-part-1
97 identifying-arma-models-part-2
98 demo-arma-model-properties
99 automatic-order-identification
100 demo-identifying-arma-orders

arima-models-trend
101 non-stationary-data-trend
102 differencing-and-integration
103 trend-functions
104 demo-trend-two-ways-in-an-arima-framework
105 the-augmented-dickey-fuller-unit-root-adf-test-part-1
106 the-augmented-dickey-fuller-unit-root-adf-test-part-2
107 demo-an-application-of-the-adf-test

arima-models-seasonality
108 seasonal-variation-part-1
109 seasonal-variation-part-2
110 the-adf-test-for-seasonality
111 demo-seasonality-two-ways-in-an-arima-framework

arimax-models-inputs
112 time-series-regression
113 demo-ordinary-regression-using-outliers
114 the-cross-correlation-function-ccf
115 the-transfer-function
116 interpreting-the-ccf
117 demo-dynamic-regression-with-event-variables
118 cross-correlation-pitfalls

bayesian-time-series-analysis

module-overview
119 about-this-module
120 classical-analysis-versus-bayesian-analysis

bayesian-time-series-structure
121 accessing-lag-and-next-values
122 demo-setting-up-autoregressive-components
123 dynamic-linear-model-setup
124 demo-setting-up-seasonality-components

exogenous-variables
125 adding-exogenous-variables
126 demo-setting-up-exogenous-components

forecasting-in-bayesian
127 preddist-and-forecasting
128 demo-forecast-output

machine-learning-approaches-to-time-series-modeling

module-overview
129 about-this-module

using-machine-learning-models-for-time-series-forecasting
130 preparing-time-series-data-for-machine-learning
131 brief-introduction-to-gradient-boosting-models
132 demo-preparing-time-series-data-and-building-a-gradient-boosting-model

deep-learning-with-recurrent-neural-networks-for-time-series-forecasting
133 introduction-to-recurrent-neural-networks
134 long-short-term-memory-blocks-in-rnns
135 demo-building-a-recurrent-neural-network-with-lstm-blocks-to-forecast-time
136 limitations-of-machine-learning-methods-for-time-series-forecasting
137 about-the-next-three-practices_instructions

hybrid-modeling-approaches-and-external-forecasts

module-overview
138 about-this-module

a-hybrid-or-ensemble-approach-to-forecasting
139 external-models-and-combined-forecasts
140 combination-forecast-details
141 combined-forecasts-using-the-tsm-package
142 demo-generating-combined-forecasts-with-the-cfc-object
143 demo-combining-forecasts-from-multiple-modeling-approaches

combining-traditional-time-series-methods-with-machine-learning-methods
144 strengths-of-machine-learning-methods-modeling-multiple-time-series
145 weighting-combined-forecasts-with-machine-learning
146 demo-using-gradient-boosting-to-find-the-best-weighted-combination-of

time-series-features

specialization-overview

welcome
147 overview
148 getting-the-most-from-this-specialization_instructions

course-overview

introduction-to-time-series-mining-and-creation
149 meet-the-instructor
150 prerequisites_instructions
151 finding-the-course-files-and-practicing-in-this-course-required_instructions
152 using-forum-and-getting-help_instructions
153 frequently-asked-questions_instructions

time-series-basics

time-series-essentials
154 introduction-to-time-series
155 time-series-data-creation

accumulation-exploration-and-binning
156 selecting-an-interval-for-accumulation-part-1
157 selecting-an-interval-for-accumulation-part-2
158 demo-accumulating-transactional-data-to-time-series
159 summary-measures-on-time-series
160 demo-exploring-time-series-summary-characteristics
161 binning-time-series
162 demo-exploring-time-series-using-binning

signal
163 signal-versus-noise
164 signal-types-and-decompositions
165 demo-decompositions-using-the-tsa-package
166 demo-feature-creation-using-the-data-step-like-syntax-in-the-tsmodel-procedure

distance-measures

sequence-distance-basics
167 introduction-to-distance-measures-for-time-series
168 measuring-sequence-similarity
169 direct-mapping-and-time-warping
170 demo-an-example-of-time-warping-and-relative-path-costs
171 demo-time-series-clustering

symbolic-representation-of-sequences
172 introduction-to-symbolic-representation-of-sequences
173 the-symbolic-aggregate-approximation-sax
174 demo-sax-distance-for-input-variable-ranking-based-on-similarity-to-the-target

spectral-analysis-and-singular-spectrum-analysis-ssa

spectral-analysis-basics
175 introduction-to-spectral-analysis
176 spectral-analysis-fundamentals
177 demo-introduction-to-spectral-analysis

the-periodogram-and-spectral-density
178 basic-ideas-from-trigonometry
179 a-regression-approach-to-cycle-identification
180 estimating-the-spectral-density
181 demo-implementing-information-from-the-periodogram

singular-spectrum-analysis
182 an-introduction-to-singular-spectrum-analysis-ssa
183 ssa-step-by-step
184 demo-singular-spectrum-analysis
185 demo-application-of-univariate-singular-spectrum-analysis

motif-analysis

motif-analysis-basics
186 introduction-to-motif-analysis
187 motif-discovery-basics
188 two-approaches-to-motif-discovery
189 demo-motif-discovery-using-the-brute-force-method
190 demo-motif-discovery-using-the-probabilistic-model-method
191 demo-motif-scoring