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
Moving Transform
Apply rolling window functions to the time series. Use this widget to get a series’ mean.
Inputs
- Time series: Time series as output by As Timeseries widget.
Outputs
- Time series: The input time series with the added series’ transformations.
In this widget, you define what aggregation functions to run over the time series and with what window sizes.
- Define a new transformation.
- Remove the selected transformation.
- Time series you want to run the transformation over.
- Desired window size.
- Aggregation function to aggregate the values in the window with. Options are: mean, sum, max, min, median, mode, standard deviation, variance, product, linearly-weighted moving average, exponential moving average, harmonic mean, geometric mean, non-zero count, cumulative sum, and cumulative product.
- Select Non-overlapping windows options if you don’t want the moving windows to overlap but instead be placed side-to-side with zero intersection.
- In the case of non-overlapping windows, define the fixed window width(overrides and widths set in (4).
Example
To get a 5-day moving average, we can use a rolling window with mean aggregation.
To integrate time series’ differences from Difference widget, use Cumulative sum aggregation over a window wide enough to grasp the whole series.