.. toctree:: :maxdepth: 2 :hidden: .. _bigmler-time-series: Time Series subcommand ====================== Using this subcommand you can generate all the resources leading to a ``time series`` and its ``forecasts``. The ``time series`` is a supervised learning model that works on an ordered sequence of data to extract the patterns needed to make ``forecasts``. The ``bigmler time-series`` subcommand will follow the steps to generate ``time series`` and predict the ``forecasts`` for every numeric field in the original dataset that has been set as objective field. As shown in the ``bigmler`` command section, the simplest call is .. code-block:: bash bigmler time-series --train data/grades.csv This command will upload the data in the ``data/grades.csv`` file and generate the corresponding ``source``, ``dataset`` and ``time series`` objects in BigML. You can use any of the intermediate generated objects to produce new time series. For instance, you could set a subgroup of the numeric fields in the dataset to be used as objective fields using the ``--objectives`` option. .. code-block:: bash bigmler time-series --dataset dataset/53b1f71437203f5ac30004ed \ --objectives "Assignment,Final" its value is expected to be a comma-separated list of fields. Similarly to the models and datasets, the generated clusters can be shared using the ``--shared`` option, e.g. .. code-block:: bash bigmler time-series --source source/53b1f71437203f5ac30004e0 \ --shared will generate a secret link for both the created dataset and time series that can be used to share the resource selectively. As models were used to generate predictions (class names in classification problems and an estimated number for regressions), time series can be used to generate forecasts, that is, to predict the value of each objective field up till the user-given horizon. The command .. code-block:: bash bigmler time-series --time-series timeseries/58437a277e0a8d38ec028a5f \ --horizon 10 would produce a file ``forecast_000001.csv`` with ten rows, one per point, and as many columns as ETS models the time series contains. When the command is executed, the time series information is downloaded to your local computer and the forecasts are computed locally, with no more latencies involved. Just in case you prefer to use BigML to compute the forecasts remotely, you can do so too .. code-block:: bash bigmler time-series --time-series timeseries/58437a277e0a8d38ec028a5f \ --horizon 10 --remote would create a remote forecast with the specified horizon. You can also specify more complex inputs for the forecast. For instance, you can set a different horizon to each objective field and you can give some criteria to select the models used in the forecast. All of this can be done using the ``--test`` option pointing to a JSON file that should contain the input to be used in the forecast as described in the `API documentation `_. As an example, let's set a horizon of 5 points for the ``Final`` field and select the first model in the time series array of ETS models, and also forecast 7 points for the ``Assignment`` field using the model with less ``aic`` (the one used by default). The command call should then be: .. code-block:: bash bigmler time-series --time-series timeseries/58437a277e0a8d38ec028a5f \ --test test.json and the ``test.json`` file should contain the following JSON: .. code-block:: json {"Final": {"horizon": 5, "ets_models": {"indices": [0]}}, "Assignment": {"horizon": 7}} Time Series Subcommand Options ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ============================================= ================================= ``--time-seriers`` *TIME_SERIES* BigML time series Id ``--time-series-set`` *PATH* Path to a file containing timeseries/ids One time series per line (e.g., timeseries/4f824203ce80051) ``--no-time-series`` No time series will be generated. ``--objectives`` *OBJECTIVES* Comma-separated list of fields that will be used in the time series as objective fields ``--time-series-attributes`` *PATH* Path to a JSON file containing attributes (any of the updatable attributes described in the `developers section `_ ) to be used in the time series creation call ``--time-series-file`` *PATH* Path to a JSON file containing the time series info ``--all-numeric-objectives`` When used, all the numeric fields in the dataset are considered objective fields ``--default-numeric-value`` *DEFAULT* The value used by default if a numeric field is missing. Spline interpolation is used by default and other options are "mean", "median", "minimum", "maximum" and "zero" ``--error`` *TYPE* Type of error considered: 1 - Additive, 2 - Multiplicative ``--period`` *PERIOD* Expected period ``--seasonality`` *SEASONALITY* Type of seasonality: 0 - None, 1 - Additive, 2 - Multiplicative ``--trend`` *TREND* Type of trend: 0 - None, 1 - Additive, 2 - Multiplicative ``--range`` *RANGE* Comma-separated pair of values that set the range limits ``--damped-trend`` When set damping is used in trend ``--forecast`` When set, the time series default forecast is produced ``--horizon`` *HORIZON* Set to an integer, is the number of points in the forecast ``--time-start`` *START* Time starting point coordinate ``--time-end`` *END* Time ending point coordinate ``--time-unit`` *UNIT* Unit for the time interval. The options are described in the `API documentation `_ ``--time-interval`` *INTERVAL* Time interval between two rows ============================================= =================================