[HN Gopher] Sktime: A Unified Python Toolbox for Machine Learnin...
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Sktime: A Unified Python Toolbox for Machine Learning with Time
Series
Author : optimalsolver
Score : 123 points
Date : 2020-09-21 09:46 UTC (13 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| mark_l_watson wrote:
| This looks very useful. I have a question for the developers, if
| any of them are here: is there any plan to add anomaly detection
| for time series data?
| prostodata wrote:
| Anomaly detection belongs to unsupervised learning while in
| time series analysis we normally think about future and future
| values are viewed as labels. One approach to think in terms of
| anomaly detection is to train a normal forecasting model. An
| anomaly is then viewed as large deviation from predicted
| values. Another approach to train an autoencoder on segments of
| the time series. Then anomaly is defined by the degree of
| deviation of the decoded segment from the real segment.
| thesehands wrote:
| Is this not a timeseries classification, which they do
| implement?
| unoti wrote:
| > Is anomaly detection not a timeseries classification, which
| they do implement
|
| It can be a time series classification if and only if you
| have __labels __that say a given sample is an anomaly. But
| then what if it runs for a while and a __new __kind of
| anomaly starts happening that it's never seen before and
| isn't in the training set: will it detect it based on your
| labels?
|
| Ideally anomaly detection should be a more unsupervised
| learning scenario where it can automatically determine what's
| normal and what's not.
| kmundnic wrote:
| I knew about work in anomaly detection for state of health
| and state of charge in batteries, where you can somewhat
| model the physical effects (in a data driven manner).
| However, this description of the problem made me think that
| meta-learning might be suitable for the problem you're
| describing. I've only seen it applied in computer vision
| though (and more recently in speech).
| nerdponx wrote:
| Anomaly detection might also refer to outlier detection, in
| that "an anomaly occurred at this point in the time series."
| TonyBagnall wrote:
| Hi, yes I'd love to add in anomaly detection, motifs and
| discords, will take time though, lack of people!
| Bostonian wrote:
| How does Sktime compare with Facebook's Prophet package, also
| callable from Python?
| mloning wrote:
| sktime is a toolbox with the goal to support multiple models
| and composition techniques, Prophet is a particular model.
| We're working on interfacing it so that you can call it using
| our API.
| nerdponx wrote:
| Sktime contains a handful of standard tools and models used for
| time series analysis and machine learning.
|
| Prophet contains one specific model developed by Facebook,
| which you will not find in Sktime.
|
| You won't ever find something like Prophet in Sktime because
| it's a "higher level" model than anything in Sktime. The
| cleverness of Prophet is based largely on its automatic feature
| engineering, with a linear regression model underneath. Whereas
| a library like Sktime focuses on implementing specific models
| like ARIMA, and letting you do your own feature engineering.
| prostodata wrote:
| In fact, there are two general approaches to unifying ML with
| time series:
|
| o Unifying ML API and patterns so that time series can be
| analyzed in the same way as normal tabular data. Example: sktime
|
| o Preprocessing libraries applying data transformations to the
| input time series by converting them to a (tabular) form which
| can be used by normal ML algorithms. These are typically general
| or specific libraries for feature engineering, feature learning,
| feature selection and generic transformations. Examples:
| https://github.com/blue-yonder/tsfresh or
| https://github.com/prostodata/prosto
|
| Which approach will win is not clear.
| mloning wrote:
| We're actually interfacing tsfresh. Unifying ML with time
| series is perhaps better understood in terms of the different
| learning tasks (e.g. time series
| classification/regression/clustering, forecasting, time series
| annotation) and their relations (applying algorithms for one
| task to help solve another).
| uoaei wrote:
| My experience has been that if you are doing feature
| engineering and using summary vectors for time series
| classification, rather than algorithms which deal with time
| series directly, and it's working, the problem is not really
| complicated enough to need high-compute machine learning
| methods and you'll be fine with one of the more popular off-
| the-shelf methods.
| hcrisp wrote:
| Sktime seems similar to DARTS, which includes both
| preprocessing and modeling patterns.
|
| https://medium.com/unit8-machine-learning-publication/darts-...
| nerdponx wrote:
| Sktime is more like "Scikit-learn for time series data",
| which notably lacks any time series methods.
| prostodata wrote:
| When I see a new forecasting library, my first question is
| whether it can apply ARIMA in a sklearn manner by training a
| model using (large) train time series X, storing the model by
| discarding the train data, and then using this model for
| predictions by feeding a completely different (and shorter) time
| series. Importantly, the time series used for prediction is not a
| continuation of the time series used for training. Moreover, we
| do not store anywhere the original time series used for training.
|
| So the question is whether sktime can apply ARIMA in this way.
| em500 wrote:
| Very unlikely that sktime could be used in this way, because
| ARIMA is rarely applied this way. This would be akin to
| estimating the mean from some sample and predicting values of a
| different population (not a different sample from the same
| population) with this mean. You could do that, but in general
| it will not yield very good predictions. ARIMA is just fitting
| the mean, variance and serial correlation of a specific time
| series. Using these sample moments to predict the trajectory of
| a completely different time series rarely makes much sense.
| prostodata wrote:
| For example, I have data from 1900 till 2000. I train ARMA
| using this data by storing the corresponding coefficients as
| model parameters. Now I get data from 2010 to 2020. My goal
| is to use these (AR and MA) coefficients in order to predict
| the value in 2021 (without using the historic data I used for
| training). I think it does make sense and it is precisely how
| typical ML algorithms work. So it is more a matter of how an
| algorithm is implemented and which usage patterns it follows.
| em500 wrote:
| Ok, if you want to apply the fitted model to later data
| points of the same series, in principle you could.
| Superficially browsing the source of sklearn, it does not
| seem to support/expose it. AFAICT, sklearn's ARIMA
| estimator wraps pmdarima, which wraps SARIMAX from
| statsmodels, which uses the statsmodels state space model
| for the actual calculations. Best I can tell, none of the
| higher lever wrappers support/expose the functionality that
| you wish. If you know how to work with the raw state space
| form in statsmodels, you could do more or less what you
| described (predict with a fitted model without retaining
| the full history - tough you also need to store the
| estimated state in addition to the ARMA coefficients).
|
| If you don't know how to do this, I'd advise you not to
| bother, unless you have a really specialistic need.
|
| ("just storing and applying fitted coefficients on new
| data" is straightforward if you have a pure AR(p) model:
| you can just plug in the coefficients in the recursive AR
| equation using the last observations. But as soon as you
| have an MA term, you have a problem, because a finite lag
| MA(q) model is equivalent to an infinite lag AR(p) model.
| You need some specialized algorithms like the innovations
| algorithm or Kalman filters to handle that. Statsmodels
| uses a Kalman filter on the state space form of the ARMA
| model.)
| nerdponx wrote:
| But there is no reason from an API perspective why it
| couldn't work like this. An ARIMA model is a handful of
| parameters that act on input and output vectors. Whether it
| makes sense to use it that way is a separate question.
|
| As it happens, this is _precisely_ how sktime works. The
| whole point is that its API is analogous to that of Scikit-
| learn. This is clearly demonstrated in the example code:
| y = load_airline() y_train, y_test =
| temporal_train_test_split(y) fh = np.arange(1,
| len(y_test) + 1) # forecasting horizon forecaster =
| ThetaForecaster(sp=12) # monthly seasonal periodicity
| forecaster.fit(y_train) y_pred =
| forecaster.predict(fh)
|
| Sktime is just an implementation of various time series
| models with a Scikit-learn-compatible API. It is still up to
| the user to know what to do with this stuff.
| mloning wrote:
| We're interfacing statsmodels and pmdarima for the
| implementation of the ARIMA model. I believe that you can
| persist models in statsmodels without saving the whole training
| data.
| denimboy wrote:
| since this package looks to be compatible with sklearn API, maybe
| it could be added to sklearn contrib page:
| https://github.com/scikit-learn-contrib
| bertolomeos wrote:
| There is a tutorial-like presentation that explains the
| motivation behind this library and how to use it from the latest
| PyData Fest. I have been watching this two thirds of the way
| through and think the presenter's explanations are worth
| listening to, if you are dealing with time series. Have a look:
| https://www.youtube.com/watch?v=Wf2naBHRo8Q
| nxpnsv wrote:
| Very nice, this seems to be a missing tool. Statsmodels offering
| is quite limited.
| nerdponx wrote:
| It's not just that the Statsmodels offering is limited, it's
| that the Scikit-learn style API is effectively an industry
| standard. Whereas the Statsmodels API is vaguely R-inspired,
| and while it has some benefits compared to Scikit-learn, it's
| doesn't fit easily into today's "standard" Python machine
| learning workflows without a bunch of wrapper code.
| nxpnsv wrote:
| This too. I like the regression report from SM, perhaps I
| must port it...
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