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
ARIMA Model
Model the time series using ARMA, ARIMA, or ARIMAX model.
Inputs
- Time series: Time series as output by As Timeseries widget.
- Exogenous data: Time series of additional independent variables that can be used in an ARIMAX model.
Outputs
- Time series model: The ARIMA 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 with ARIMA model.
- Model’s name. By default, the name is derived from the model and its parameters.
- ARIMA’s p, d, q parameters.
- Use exogenous data. Using this option, you need to connect additional series on the Exogenous data input signal.
- Number of forecast steps the model should output, along with the desired confidence intervals values at each step.