TSML (Time Series Machine Learning) is a 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.
Over the past years, the industrial sector has seen many innovations brought about by automation. Inherent in this automation is the installation of sensor networks for status monitoring and data collection. One of the major challenges in these data-rich environments is how to extract and exploit information from these large volume of data to detect anomalies, discover patterns to reduce downtimes and manufacturing errors, reduce energy usage, etc.
To address these issues, we developed TSML package. It leverages AI and ML libraries from ScikitLearn, Caret, and Julia as building blocks in processing huge amount of industrial time series data. It has the following characteristics described below.
- TS data type clustering/classification for automatic data discovery
- TS aggregation based on date/time interval
- TS imputation based on Nearest Neighbors
- TS statistical metrics for data quality assessment
- TS ML wrapper more than 100+ libraries from caret, scikitlearn, and julia
- TS date/value matrix conversion of 1-D TS using sliding windows for ML input
- Common API wrappers for ML libs from JuliaML, PyCall, and RCall
- Pipeline API allows high-level description of the processing workflow
- Specific cleaning/normalization workflow based on data type
- Automatic selection of optimised ML model
- Automatic segmentation of time-series data into matrix form for ML training and prediction
- Easily extensible architecture by using just two main interfaces: fit and transform
- Meta-ensembles for robust prediction
- Support for distributed computation for scalability and speed
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 TSML
julia> using Pkg julia> pkg"add TSML"
julia> using Pkg julia> Pkg.add("TSML")
julia> pkg"add TSML"
Once TSML is installed, you can load the TSML package by:
julia> using TSML
julia> import TSML
Generally, you will need the different transformers and utils in TSML for time-series processing. To use them, TSML relies on the Reexport.jl package to exposes all the necessary filters and transformers into the Julia Main module including exported functions in DataFrames, CSV, Dates, and Random. By just a single line below, all these related modules become available in Julia Main module:
- Aggregators and Imputers
- Statistical Metrics
- Monotonic Detection and Plotting
- TS Data Discovery
- Value Preprocessing
- Date Preprocessing