tstoolbox.tstoolbox.forecast

tstoolbox.tstoolbox.forecast(input_ts='-', start_date=None, end_date=None, columns=None, dropna='no', round_index=None, clean=False, target_units=None, source_units=None, skiprows=None, index_type='datetime', names=None, horizon=2, print_input=False, print_cols='all')

Machine learning automatic forecasting

Machine learning forecast using PyAF (Python Automatic Forecasting)

Uses a machine learning approach (The signal is cut into estimation and validation parts, respectively, 80% and 20% of the signal). A time-series cross-validation can also be used.

Forecasting a time series model on a given horizon (forecast result is also pandas data-frame) and providing prediction/confidence intervals for the forecasts.

Generic training features

• Signal decomposition as the sum of a trend, periodic and AR component

• Works as a competition between a comprehensive set of possible signal transformations and linear decompositions. For each transformed signal , a set of possible trends, periodic components and AR models is generated and all the possible combinations are estimated. The best decomposition in term of performance is kept to forecast the signal (the performance is computed on a part of the signal that was not used for the estimation).

• Signal transformation is supported before signal decompositions. Four transformations are supported by default. Other transformation are available (Box-Cox etc).

• All Models are estimated using standard procedures and state-of-the-art time series modeling. For example, trend regressions and AR/ARX models are estimated using scikit-learn linear regression models.

• Standard performance measures are used (L1, RMSE, MAPE, etc)

Exogenous Data Support

• Exogenous data can be provided to improve the forecasts. These are expected to be stored in an external data-frame (this data-frame will be merged with the training data-frame).

• Exogenous data are integrated in the modeling process through their past values (ARX models).

• Exogenous variables can be of any type (numeric, string , date, or object).

• Exogenous variables are dummified for the non-numeric types, and standardized for the numeric types.

Hierarchical Forecasting

• Bottom-Up, Top-Down (using proportions), Middle-Out and Optimal Combinations are implemented.

• The modeling process is customizable and has a huge set of options. The default values of these options should however be OK to produce a reasonable quality model in a limited amount of time (a few minutes).

Parameters:

• input_ts (str) –

  [optional though required if using within Python, default is ‘-’ (stdin)]

  Whether from a file or standard input, data requires a single line header of column names. The default header is the first line of the input, but this can be changed for CSV files using the ‘skiprows’ option.

  Most common date formats can be used, but the closer to ISO 8601 date/time standard the better.

  Comma-separated values (CSV) files or tab-separated values (TSV):

  File separators will be automatically detected.
  
  Columns can be selected by name or index, where the index for
  data columns starts at 1.
  

  Command line examples:

  Keyword Example	Description
  –input_ts=fn.csv	read all columns from ‘fn.csv’
  –input_ts=fn.csv,2,1	read data columns 2 and 1 from ‘fn.csv’
  –input_ts=fn.csv,2,skiprows=2	read data column 2 from ‘fn.csv’, skipping first 2 rows so header is read from third row
  –input_ts=fn.xlsx,2,Sheet21	read all data from 2nd sheet all data from “Sheet21” of ‘fn.xlsx’
  –input_ts=fn.hdf5,Table12,T2	read all data from table “Table12” then all data from table “T2” of ‘fn.hdf5’
  –input_ts=fn.wdm,210,110	read DSNs 210, then 110 from ‘fn.wdm’
  –input_ts=’-’	read all columns from standard input (stdin)
  –input_ts=’-’ –columns=4,1	read column 4 and 1 from standard input (stdin)

  If working with CSV or TSV files you can use redirection rather than use –input_ts=fname.csv. The following are identical:

  From a file:

  command subcmd –input_ts=fname.csv

  From standard input (since ‘–input_ts=-’ is the default:

  command subcmd < fname.csv

  Can also combine commands by piping:

  command subcmd < filein.csv | command subcmd1 > fileout.csv

  Python library examples:

  You must use the `input_ts=...` option where `input_ts` can be
  one of a [pandas DataFrame, pandas Series, dict, tuple, list,
  StringIO, or file name].
  

• start_date (str) –

  [optional, defaults to first date in time-series, input filter]

  The start_date of the series in ISOdatetime format, or ‘None’ for beginning.

• end_date (str) –

  [optional, defaults to last date in time-series, input filter]

  The end_date of the series in ISOdatetime format, or ‘None’ for end.

• skiprows (list-like or integer or callable) –

  [optional, default is None which will infer header from first line, input filter]

  Line numbers to skip (0-indexed) if a list or number of lines to skip at the start of the file if an integer.

  If used in Python can be a callable, the callable function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise. An example of a valid callable argument would be

  lambda x: x in [0, 2].

• names (str) –

  [optional, default is None, transformation]

  If None, the column names are taken from the first row after ‘skiprows’ from the input dataset.

  MUST include a name for all columns in the input dataset, including the index column.

• columns –

  [optional, defaults to all columns, input filter]

  Columns to select out of input. Can use column names from the first line header or column numbers. If using numbers, column number 1 is the first data column. To pick multiple columns; separate by commas with no spaces. As used in toolbox_utils pick command.

  This solves a big problem so that you don’t have to create a data set with a certain column order, you can rearrange columns when data is read in.

• dropna (str) –

  [optional, defauls it ‘no’, input filter]

  Set dropna to ‘any’ to have records dropped that have NA value in any column, or ‘all’ to have records dropped that have NA in all columns. Set to ‘no’ to not drop any records. The default is ‘no’.

• clean –

  [optional, default is False, input filter]

  The ‘clean’ command will repair a input index, removing duplicate index values and sorting.

• source_units (str) –

  [optional, default is None, transformation]

  If unit is specified for the column as the second field of a ‘:’ delimited column name, then the specified units and the ‘source_units’ must match exactly.

  Any unit string compatible with the ‘pint’ library can be used.

• target_units (str) –

  [optional, default is None, transformation]

  The purpose of this option is to specify target units for unit conversion. The source units are specified in the header line of the input or using the ‘source_units’ keyword.

  The units of the input time-series or values are specified as the second field of a ‘:’ delimited name in the header line of the input or in the ‘source_units’ keyword.

  Any unit string compatible with the ‘pint’ library can be used.

  This option will also add the ‘target_units’ string to the column names.

• round_index –

  [optional, default is None which will do nothing to the index, output format]

  Round the index to the nearest time point. Can significantly improve the performance since can cut down on memory and processing requirements, however be cautious about rounding to a very course interval from a small one. This could lead to duplicate values in the index.

• index_type (str) –

  [optional, default is ‘datetime’, output format]

  Can be either ‘number’ or ‘datetime’. Use ‘number’ with index values that are Julian dates, or other epoch reference.

• print_input –

  [optional, default is False, output format]

  If set to ‘True’ will include the input columns in the output table.

• tablefmt (str) –

  [optional, default is ‘csv’, output format]

  The table format. Can be one of ‘csv’, ‘tsv’, ‘plain’, ‘simple’, ‘grid’, ‘pipe’, ‘orgtbl’, ‘rst’, ‘mediawiki’, ‘latex’, ‘latex_raw’ and ‘latex_booktabs’.

• horizon (int) – Number of intervals to forecast.

• print_cols (str) – Identifies what columns to return. One of “all” or “forecast”