Home

Awesome

<div align="center">

Visitor

<img src="https://ibm.github.io/TSML.jl/tsmllogo/tsmllogo13.png" alt="TSML Logo" width="250"> </img>

OpenSSF Best Practices

Overall Stats

DocumentationBuild StatusHelp
</div>

Stargazers over time

Stargazers over time

TSML (Timeseries Machine Learning) Visitor

TSML is a package for time series data processing, classification, clustering, and prediction. It combines ML libraries from Python's ScikitLearn (thru its complementary AutoMLPipeline package) and Julia MLs using a common API and allows seamless ensembling and integration of heterogenous ML libraries to create complex models for robust time-series prediction. The design/framework of this package is influenced heavily by Samuel Jenkins' Orchestra.jl and CombineML.jl packages. TSML is actively developed and tested in Julia 1.0 and above for Linux, MacOS, and Windows.

Links to TSML demo, tutorial, and published JuliaCon paper:

Package Features

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> ]
(v1.1) pkg> add TSML

Or, equivalently, via the Pkg API:

julia> using Pkg
julia> Pkg.add("TSML")

Motivations

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 and Julia as building blocks in processing huge amount of industrial times series data. It has the following characteristics described below.

Main Workflow

The package assumes a two-column input composed of Dates and Values. The first part of the workflow aggregates values based on the specified date/time interval which minimizes occurrence of missing values and noise. The aggregated data is then left-joined to the complete sequence of dates in a specified date/time interval. Remaining missing values are replaced by k nearest neighbors where k is the symmetric distance from the location of missing value. This approach can be called several times until there are no more missing values.

TSML uses a pipeline of filters and transformers which iteratively calls the fit! and transform! families of functions relying on multiple dispatch to select the correct algorithm from the steps outlined above.

TSML supports transforming time series data into matrix form for ML training and prediction. Dateifier filter extracts the date features and convert the values into matrix form parameterized by the size and stride of the sliding window representing the dimension of the input for ML training and prediction. Similar workflow is done by the Matrifier filter to convert the time series values into matrix form.

The final part combines the dates matrix with the values matrix to become input of the ML with the output representing the values of the time periods to be predicted ahead of time.

Machine learning functions in TSML are wrappers to the corresponding Scikit-learn and native Julia ML libraries. There are more than hundred classifiers and regression functions available using a common API. In order to access these Scikit-learn wrappers, one should load the related package called AutoMLPipeline.

Below are examples of the Pipeline workflow.

# Setup source data and filters to aggregate and impute hourly
using TSML 

fname        = joinpath(dirname(pathof(TSML)),"../data/testdata.csv")
csvread      = CSVDateValReader(Dict(:filename=>fname,:dateformat=>"dd/mm/yyyy HH:MM"))
aggregate    = DateValgator(Dict(:dateinterval=>Dates.Hour(1)))   # aggregator
impute       = DateValNNer(Dict(:dateinterval=>Dates.Hour(1)))    # imputer
chkstats     = Statifier(Dict(:processmissing=>true))             # get statistics
normtonic    = Monotonicer(Dict()) # normalize monotonic data
chkoutlier   = Outliernicer(Dict(:dateinterval => Dates.Hour(1))) # normalize outliers

pipexpr = csvread
data    = fit_transform!(pipexpr)
first(data,5)

5×2 DataFrame
│ Row │ Date                │ Value   │
│     │ DateTime            │ Float64 │
├─────┼─────────────────────┼─────────┤
│ 1   │ 2014-01-01T00:06:00 │ 10.0    │
│ 2   │ 2014-01-01T00:18:00 │ 10.0    │
│ 3   │ 2014-01-01T00:29:00 │ 10.0    │
│ 4   │ 2014-01-01T00:40:00 │ 9.9     │
│ 5   │ 2014-01-01T00:51:00 │ 9.9     │

pipexpr = csvread |> aggregate |> chkstats
stats   = fit_transform!(pipexpr)

1×26 DataFrame. Omitted printing of 19 columns
│ Row │ tstart              │ tend                │ sfreq    │ count │ max     │ min     │ median  │
│     │ DateTime            │ DateTime            │ Float64  │ Int64 │ Float64 │ Float64 │ Float64 │
├─────┼─────────────────────┼─────────────────────┼──────────┼───────┼─────────┼─────────┼─────────┤
│ 1   │ 2014-01-01T00:00:00 │ 2015-01-01T00:00:00 │ 0.999886 │ 3830  │ 18.8    │ 8.5     │ 10.35   │

Note: fit_transform! is equivalent to calling in sequence fit! and transform! functions.

pipexpr = csvread |> aggregate |> impute |> chkstats
stats2  = fit_transform!(pipexpr)

1×26 DataFrame. Omitted printing of 19 columns
│ Row │ tstart              │ tend                │ sfreq    │ count │ max     │ min     │ median  │
│     │ DateTime            │ DateTime            │ Float64  │ Int64 │ Float64 │ Float64 │ Float64 │
├─────┼─────────────────────┼─────────────────────┼──────────┼───────┼─────────┼─────────┼─────────┤
│ 1   │ 2014-01-01T00:00:00 │ 2015-01-01T00:00:00 │ 0.999886 │ 8761  │ 18.8    │ 8.5     │ 10.0    │

pipexpr = csvread |> aggregate |> impute |> normtonic  
fit_transform!(pipexpr)

8761×2 DataFrame
│ Row  │ Date                │ Value    │
│      │ DateTime            │ Float64? │
├──────┼─────────────────────┼──────────┤
│ 1    │ 2014-01-01T00:00:00 │ 10.0     │
│ 2    │ 2014-01-01T01:00:00 │ 9.9      │
│ 3    │ 2014-01-01T02:00:00 │ 10.0     │
│ 4    │ 2014-01-01T03:00:00 │ 10.0     │
│ 5    │ 2014-01-01T04:00:00 │ 10.0     │
│ 6    │ 2014-01-01T05:00:00 │ 10.0     │
│ 7    │ 2014-01-01T06:00:00 │ 10.0     │
⋮

# create artificial timeseries data
datets  = DateTime(2018,1,1):Dates.Day(1):DateTime(2019,1,31) |> collect
valuets = rand(1:100,length(datets))
ts      = DataFrame(Date=datets,Value=valuets)
@show first(ts,5);

5×2 DataFrame
│ Row │ Date                │ Value │
│     │ DateTime            │ Int64 │
├─────┼─────────────────────┼───────┤
│ 1   │ 2018-01-01T00:00:00 │ 56    │
│ 2   │ 2018-01-02T00:00:00 │ 93    │
│ 3   │ 2018-01-03T00:00:00 │ 40    │
│ 4   │ 2018-01-04T00:00:00 │ 15    │
│ 5   │ 2018-01-05T00:00:00 │ 78    │

# Pipeline to concatinate matrified value and date series
args     = Dict(:ahead => 24,:size => 24,:stride => 5)
datemtr  = Dateifier(args)
valuemtr = Matrifier(args)
ppl      = datemtr + valuemtr
dateval  = fit_transform!(ppl,ts)
first(dateval,5)

5×33 DataFrame. Omitted printing of 21 columns
│ Row │ year  │ month │ day   │ hour  │ week  │ dow   │ doq   │ qoy   │ x1    │ x2    │ x3    │ x4    │
│     │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┤
│ 1   │ 2019  │ 1     │ 7     │ 0     │ 2     │ 1     │ 7     │ 1     │ 94    │ 97    │ 18    │ 76    │
│ 2   │ 2019  │ 1     │ 2     │ 0     │ 1     │ 3     │ 2     │ 1     │ 99    │ 93    │ 65    │ 68    │
│ 3   │ 2018  │ 12    │ 28    │ 0     │ 52    │ 5     │ 89    │ 4     │ 88    │ 8     │ 59    │ 1     │
│ 4   │ 2018  │ 12    │ 23    │ 0     │ 51    │ 7     │ 84    │ 4     │ 76    │ 5     │ 6     │ 92    │
│ 5   │ 2018  │ 12    │ 18    │ 0     │ 51    │ 2     │ 79    │ 4     │ 6     │ 54    │ 66    │ 72    │

We can use the matrified dateval as input features for prediction/classication. Let's create a dummy response consisting of yes or no and use Random Forest to learn the mapping. More examples of ML modeling can be found in TSML's complementary packages: AutoMLPipeline and AMLPipelineBase.

target        = rand(["yes","no"],nrow(dateval))
rf            = RandomForest()
accuracy(x,y) = score(:accuracy,x,y)
crossvalidate(rf,dateval,target,accuracy)

fold: 1, 14.285714285714285
fold: 2, 57.14285714285714
fold: 3, 71.42857142857143
fold: 4, 85.71428571428571
fold: 5, 57.14285714285714
fold: 6, 57.14285714285714
fold: 7, 57.14285714285714
fold: 8, 71.42857142857143
fold: 9, 42.857142857142854
fold: 10, 71.42857142857143
(mean = 58.57142857142857, std = 19.57600456294711, folds = 10)

Extending TSML

If you want to add your own filter or transformer or learner, take note that filters and transformers process the input features but ignores the output argument. On the other hand, learners process both their input and output arguments during fit! while transform! expects one input argument in all cases.

The first step is to import the abstract types and define your own mutable structure as subtype of either Learner or Transformer. Next is to import the fit! and transform! functions so that you can overload them. Also, you must load the DataFrames package because it is the main format for data processing. Finally, implement your own fit and transform and export them.

  using DataFrames
  using TSML.AbsTypes

  # import functions for overloading
  import TSML.AbsTypes: fit!, transform!

  # export the new definitions for dynamic dispatch
  export fit!, transform!, MyFilter

  # define your filter structure
  mutable struct MyFilter <: Transformer
    name::String
    model::Dict
    args::Dict
    function MyFilter(args::Dict())
        ....
    end
  end

# define your fit! function.
  function fit!(fl::MyFilter, inputfeatures::DataFrame, target::Vector=Vector())
       ....
  end

  #define your transform! function
  function transform!(fl::MyFilter, inputfeatures::DataFrame)::DataFrame
       ....
  end

Remember that the main format to exchange data is dataframe which requires transform! output to return a dataframe. The features as input for fit! and transform! shall be in dataframe format too. This is necessary so that the pipeline passes the dataframe format consistently to its corresponding filters or transformers or learners. Once you have create this transformer, you can use plug is as part of the pipeline element together with the other learners and transformers.

Feature Requests and Contributions

We welcome contributions, feature requests, and suggestions. Here is the link to open an issue for any problems you encounter. If you want to contribute, please follow the guidelines in contributors page.

Help usage

Usage questions can be posted in: