TSML (Time-Series Machine Learning)
TSML (Time Series Machine Learning) is package for Time Series data processing, classification, and prediction. It combines ML libraries from Python's ScikitLearn, R's Caret, and Julia ML using a common API and allows seamless ensembling and integration of heterogenous ML libraries to create complex models for robust time-series pre-processing and prediction/classification.
Package Features
- TS aggregation based on time/date interval
- TS imputation based on Nearest Neighbors
- TS statistical metrics of data quality
- TS classification for automatic data discovery
- TS prediction with more than 100+ libraries from caret, scikitlearn, and julia
- TS date/val matrix conversion of 1-d TS using sliding windows for ML input
- Pipeline API allows high-level description of the processing workflow
- Easily extensible architecture by using just two main interfaces: fit and transform
- Support for hundreds of external ML libs from Scikitlearn and Caret by using common API wrappers for PyCall and RCall
Installation
TSML is in the Julia Official package registry. The latest release can be installed at the Julia prompt using Julia's package management which is triggered by pressing ] at the julia prompt:
julia> ]
(v1.0) pkg> add TSMLor
julia> using Pkg
julia> pkg"add TSML"or
julia> using Pkg
julia> Pkg.add("TSML")or
julia> pkg"add TSML"Once TSML is installed, you can load the TSML package by:
julia> using TSMLor
julia> import TSMLGenerally, you will need the different transformers and utils in TSML for time-series processing. To use them, it is standard in TSML code to have the following declared at the topmost part of your application:
using TSML
using TSML.TSMLTransformers
using TSML.TSMLTypes
using TSML.UtilsTutorial Outline
- Aggregators and Imputers
- Pipeline
- Statistical Metrics
- Statifier for Both Non-Missing and Missing Values
- Statifier for Non-Missing Values only
- Statifier After Imputation
- Monotonic Detection
- TS Data Discovery