Widgets
Data
-
File
-
CSV File Import
-
Datasets
-
SQL Table
-
Data Table
-
Paint Data
-
Data Info
-
Data Sampler
-
Select Columns
-
Select Rows
-
Pivot Table
-
Rank
-
Correlations
-
Merge Data
-
Concatenate
-
Select by Data Index
-
Transpose
-
Randomize
-
Preprocess
-
Apply Domain
-
Impute
-
Outliers
-
Edit Domain
-
Python Script
-
Create Instance
-
Color
-
Continuize
-
Create Class
-
Discretize
-
Feature Constructor
-
Feature Statistics
-
Neighbors
-
Purge Domain
-
Save Data
Visualize
Model
Evaluate
Unsupervised
Spectroscopy
Text Mining
-
Corpus
-
Import Documents
-
The Guardian
-
NY Times
-
Pubmed
-
Twitter
-
Wikipedia
-
Preprocess Text
-
Corpus to Network
-
Bag of Words
-
Document Embedding
-
Similarity Hashing
-
Sentiment Analysis
-
Tweet Profiler
-
Topic Modelling
-
Corpus Viewer
-
Word Cloud
-
Concordance
-
Document Map
-
Word Enrichment
-
Duplicate Detection
-
Statistics
Bioinformatics
Single Cell
Image Analytics
Networks
Geo
Educational
Time Series
Associate
VAR Model
Model the time series using vector autoregression (VAR) model.
Inputs
- Time series: Time series as output by As Timeseries widget.
Outputs
- Time series model: The VAR model fitted to input time series.
- Forecast: The forecast time series.
- Fitted values: The values that the model was actually fitted to, equals to original values - residuals.
- Residuals: The errors the model made at each step.
Using this widget, you can model the time series using VAR model.
- Model’s name. By default, the name is derived from the model and its parameters.
- Desired model order (number of parameters).
- If other than None, optimize the number of model parameters (up to the value selected in (2)) with the selected information criterion (one of: AIC, BIC, HQIC, FPE, or a mix thereof).
- Choose this option to add additional “trend” columns to the data:
- Constant: a single column of ones is added
- Constant and linear: a column of ones and a column of linearly increasing numbers are added
- Constant, linear and quadratic: an additional column of quadratics is added
- Number of forecast steps the model should output, along with the desired confidence intervals values at each step.