Recent Releases of autots
autots - 0.6.18
0.6.18 🇺🇦 🇺🇦 🇺🇦
- minor tweaks and bug fixes
- updated python version required to 3.9 to allow more typing options
- validation_indexes can now be a list of tuples with mixed forecast lengths
- "mixed_length" validation option, for limited testing of longer forecast lengths
- kalman method for ensemble aggregation (ala sensor fusion)
- UpscaleDownscaleTransformer added
- PreprocessingExperts model added (experimental)
- Python
Published by winedarksea over 1 year ago
autots - 0.6.16
0.6.16 🇺🇦 🇺🇦 🇺🇦
- exporttemplate added focusmodels option
- added OneClassSVM and GaussianMixture anomaly model options
- added plotunpredictabilityscore
- added a few more NeuralForecast search options
- bounds_only to Constraint transformer
- updates for deprecated upstream args
- FIRFilter transformer added
- mle and imle downscaled to reduce score imbalance issues with these two in generate score
- SectionalMotif now more robust to forecast lengths longer than history
- new transformer and metric options for SectionalMotif
- NaN robustness to matse
- 'round' option to Constraint
- minor change to mosaic min style ensembles to remove edge case errors
- 'mosaic-profile', 'filtered', 'unpredictability_adjusted' and 'median' style mosaics added
- updated profiler, and improved feature generation for horizontal generalization
- changepoint style trend as an option to GLM and GLS
- added ShiftFirstValue which is only a minor nuance on PositiveShift transformer
- added BasicLinearModel model
- datepart_method, scale, and fourier encodig to WindowRegression
- trimmed_mean and more date part options to SeasonalityMotif
- some additional options to MultivariateRegression
- added ThetaTransformer
- added TVVAR model (time varying VAR)
- added ChangepointDetrend transformer
- added MeanPercentSplitter transformer
- updated load_daily with more recent history
- added support for passing a custom metric
- Python
Published by winedarksea over 1 year ago
autots - 0.6.15
0.6.15 🇺🇦 🇺🇦 🇺🇦
- Constraint transformer added
- historical_growth constraint method added
- fft as multivariate_feature for Cassandra
- None trend_window now searched as part of Cassandra
- "quarterlydayofweek" method added for datepart
- threshold_method arg to AlignLastValue
- general tempate updated
- slight change to MATSE metric, now only abs values for scaling
- additional args to DatepartRegression
- bug fixes
- Python
Published by winedarksea almost 2 years ago
autots - 0.6.14
0.6.14 🇺🇦 🇺🇦 🇺🇦
- prevent excessive use of 'CenterSplit' and other macro_micro style transformers
- added ElasticNetwork as subsidiary regression model option
- KalmanSmoothing, BKBandpassFilter added on_inverse option
- add threshold arg to AlignLastValue
- Python
Published by winedarksea about 2 years ago
autots - 0.6.12
0.6.12 🇺🇦 🇺🇦 🇺🇦
- bug fixes
- added DMD model
- modified the
constraintsoptions so it now accepts of list of dictionaries of constraints with new last_window and slope options - 'dampening' as a constraint method to dampen all forecasts, fixed Cassandra trend_phi dampening
- new med_diff anomaly method and 'laplace' added as distribution option
- modified fourier_df to now work with sub daily data
- some madness with wavelets attempting to use them like fourier series for seasonality
- Python
Published by winedarksea about 2 years ago
autots - 0.6.11
0.6.11 🇺🇦 🇺🇦 🇺🇦
- bug fixes
- continually trying to keep up with the Pandas maintainers who are breaking stuff for no good reasonable
- updated RollingMeanTransformer and RegressionFilter, RegressionFilter should now be less memory intensive
- EIA data call to loadlivedaily
- horizontalensemblevalidation arg for more complete validation on these ensembles
- Python
Published by winedarksea about 2 years ago
autots - 0.6.3
0.6.3 🎐🎐🎐
- energy datasets to loadlivedaily
- improved the 'Scalable' transformer_list to reduce memory issues on larger datasets
- memory improvements to KalmanSmoother, HolidayTransformer, LocalLinearTrend
- added DiffSmoother
- added force_gc arg which can be tried if memory is in short supply relative to data (probably won't help much)
- Python
Published by winedarksea over 2 years ago
autots - 0.6.2
0.6.2 🏡🏡🏡
- FFTFilter added
- FFT model added
- kdtree to Univariate/Multivariate Motif
- additional metrics to MetricMotif
- added BallTreeMultivariateMotif model
- added FFTDecomposition
- added ReplaceConstant
- added TiDE model
- logging improvements for pytorch and some pytorch backend for gluonts support
- added AlignLastDiff
- added plotmetriccorr
- added mate, wasserstein, dwd metrics
- Python
Published by winedarksea over 2 years ago
autots - 0.6.1
0.6.1 ✨✨✨
- CenterSplit transformer added
- separated function for best model selection
- improved graphing when forecast_length == 1
- quieter Prophet
- added PreprocessingRegression model
- more KalmanStateSpace models
- assorted bug fixes
- added AutoTS diagnose_params
- Python
Published by winedarksea over 2 years ago
autots - 0.6.0
0.6.0 ♨️♨️♨️
- importbestmodel and fit_data
- added canberra distance to SeasonalityMotif and MetricMotif
- DatepartRegressionTransformer now handles NaN in input data
- new DatepartRegression and SeasonalityMotif based imputers
- LevelShiftMagic transformer added
- adjusted automatic resampling to be performed only when necessary (to avoid filling NA with 0 bug and aggfunc='sum')
- fixed a bug where if a full validation round fails, best model selection fails
- macro_micro to LocalLinearTrend
- Python
Published by winedarksea almost 3 years ago
autots - 0.5.8
0.5.8 🌷🌷🌷
- added plot_validations
- updated pytorch forecasting for 1.0.0 version
- plot_grid to prediction object
- breaking change: plotperseriessmape switched to the more accurately described plotperseriesmape
- various bug fixes such as 'TotalRuntime' missing
- Python
Published by winedarksea almost 3 years ago
autots - 0.5.4
0.5.4 🌏 🌏 🌏
- Cassandra seasonality, holiday detection, and other bug fixes
- attempted to improve the KalmanStateSpace and KalmanSmoothing methods
- validation indexes now generated in a standalone function
- create_regressor bug fix
- added MLEnsemble
- fix a bug where common_fourier failed on short length forecasts
- added some additional options to ARDL
- refactored metric df eval
- refactored parts of AutoTS for templates and validations
- added ewmae metric
- non Horizontal ensembles are now also constructed after validations and vals are rerun
- Python
Published by winedarksea over 3 years ago
autots - 0.5.3
0.5.3 🌍 🌍 🌍
- robustness changes to generatescoreper_series for horizontal ensembles
- added generation_timeout to allow stopping based on time
- added ability to specify anomaly model (ie 'zscore') to AnomalyDetector.getnewparams(method='zscore')
- fixed a bug in Cassandra with futureimpacts and futureregressor
- Python
Published by winedarksea over 3 years ago
autots - 0.5.1
0.5.1 :mageman: :mageman: :mage_man:
- add LocalLinearTrend transformer
- improved ScipyFilter focusing on Butter and Savitzky–Golay filters
- update to AutoTS().back_forecast including breaking change of
columnarg renamed toseries - add KalmanSmoothing transformer
- add KalmanStateSpace model (Kalman Filter + 'any' state space models)
- AlignLastValue no longer applied to upper/lower forecast bounds
- modified
regressionimpact in HolidayTransformer to weighted least squares, moved existing to 'datepart_regression' - added Cassandra model
- bug fix for Categorical dateparts with 1 starts
- updated load_daily to Wikimedia page views
- added dwae metric
- added MetricMotif model
- added 'common_fourier' datepart method
- added SeasonalMotif model
- added 'seasonal' validation
- bug fix for cffilter with univariate input
- Python
Published by winedarksea over 3 years ago
autots - 0.5.0
0.5.0 :spaceinvader: :spaceinvader: :space_invader:
- added AnomalyDetector
- added HolidayDetector
- added observationend and Wikipedia data to loadlive_daily
- added binarized versions of datepart method (should have done ages ago!)
- addded RRVAR, MAR, TMF, LATC models
- added AlignLastValue transformer
- added plothorizontalmodel_count and fixed an error in horizontal generation plot
- adjusted TotalRuntime to higher precision, and no longer + 1
- added subsidiary transformer for cleaning in Detrend and Datepart detrend Transformers
- sped up SinTrend transformer
- new AnomalyRemoval transformer
- added HolidayTransformer
- added auto holidays to Prophet
- added getnewparams method to AutoTS class
- more holidays options to create_regressor
- Python
Published by winedarksea almost 4 years ago
autots - 0.4.2
Latest :spaceinvader: :spaceinvader: :space_invader:
- plothorizontalpergeneration and horizontalper_generation added
- model_list now accepts a dictionary of probabilities, however this only affects new Random Templates
- :seedling: improved the genetic algorithm for new model generation
- minor improvement to generatescoreper_series for handling very small ~e-20 errors
- added PytorchForecasting to available models
- Johansen Cointegration transformer
- BTCD transformer
- Johansen and BTCD as Regression features
- fixed bug in plot_horizontal()
- added ARCH to available models
- changed the sklearn models used by UnivariateRegression by default and returned to default model_list
- fixed a bug in KerasRNN
- Python
Published by winedarksea almost 4 years ago
autots - 0.4.1
Latest :fireworks: :fireworks:
- Replaced ZeroesNaive model with ConstantNaive
- updated the General starting template
- added 'window' to AverageValueNaive
- added :octocat: load_artificial sample dataset
- fixed bug in plot_horizontal where not handling negative series
- major update to Constraint functionality
- GluonTS is no longer part of the default model list (faster tests this way) but is now part of 'best'
- horizontal modelstouse for Mosaic ensembles
- added some intel optimizations to sklearn code if scikit-learn-intelex installed
- fixed a :bug: in rollingxregressor where datepart method wasn't actually getting appended
- sped up rollingxregressor by reducing concats
- added currentmodelfile as an option for additional debugging information
- updated PCA and FastICA to be more flexible on n_components
- fixed a bug where unpackensemblemodels with keep_ensemble=False was still keeping nested ensembles
- impute speed optimizations for fill_mean, use of nan check so runs faster if no nan present
- optimizations to metrics including faster if no NaN present
- :heavyplussign: faster percentile function, used in transforms, basics
- also added nan_checks to switch between numpy na and numpy non-na quantiles/medians
- made fakedatefill nan method vectorized
- slightly adjusted the upper/lower forecast method for LastValueNaive :crystal_ball:
- updated slidingwindowview to allow Motifs to run on Numpy < 1.20 and also for faster WindowRegression
- updated regressorused and usedregressor_check so they should be more reliably filled.
- changed behavior where importtemplates would fail if modellist not satisified. Still fails, but now only for the "only" import option
- added :chartwithupwards_trend: when a model in validation is the best so far that round
- addition of "auto" and "max" num_validations with auto set as default
- addition of new metrics: mqae, oda, maxe
- Python
Published by winedarksea about 4 years ago
autots - 0.4.0
Latest
- Note: the plan is to replace ZeroesNaive model with ConstantNaive in a future release
- fix bug where score was failing to generate if madeweighting > 0 and forecastlength = 1
- made MADE functional on forecastlength=1 if dftrain is provided
- SMAPE now in repr of fit AutoTS class
- contour now works on forecast_length = 1
- Added NeuralProphet model
- made the probabilistic modeling of MultivariateRegression a parameter which only occurs when 'deep' mode is active (too slow)
- added more params to pass through to Prophet
- add phi damping to Detrend transformer
- add window slice to Detrend transformer
- added 'simple_2" datepart method
- added package to conda-forge
- preclean method added to AutoTS
- added median and midhinge point_methods to BestN Ensembles
- added additional model selections to 'simple' and 'subsample' ensemble
- switch LightGBM back to MultiOutputRegressor from RegressorChain for speed (with LightGBMRegressorChain replacing)
- removed top-level datasets
requestsdependency - Added EWMAFilter
- improved NaN in forecast check
- added failonforecastnan (bool) to AutoTS.predict and modelforecast, if False, can now allow forecasts with NaN to be returned
- add returnmodel to modelforecast for model and transformer
- fixed bug where Detrend failing with non-datetime index
- improved error handling in Transformers to explicitly reference which failed
- added random.seed() setting in AutoTS which actually seems to standardize the runs
- sped up assembling/concat of horizontal ensembles for large numbers of series
- added polynomialdegree to datepart (and ~Transformer and ~Regression)
- updated infer_frequency and utilized in model base class
- added additional datasets (analytics.gov and severe weather) to loadlivelydaily and modified pytrends load
- added rps to metrics (although no plans to build it into evaluation)
- added 'Ridge' and 'GaussianProcessRegressor' as model options for Regressions
- enforcing consistency on inner n_jobs with MultiOutputRegressor
- add DynamicFactorMQ model from Statsmodels
- added plotperseriessmape and listfailedmodeltypes to output more run information from AutoTS class
- increased number of best per series models added to models to validate (models to validate has become more of a baseline than a firm number)
- finally transitioned
ensembleparameter fully to a list from the original comma-sep string list - MLE and iMLE logarithmic metrics for targeting under- and over- estimation
- MAGE metric for error on rollup forecasts
- Mosaic ensembles now include a metric_weighting variation including MAE, RMSE and SPL weighting (abs error, square error, pl error) (unscaled)
- minor but noticeable speedups to TemplateWizard and inferred_normal functions
- added EventRiskForecast for determing risk of exceeding limits (should be considered in beta for now)
- backforecast now has tail/evalperiods configured
- changed behavior of importtemplate, by default simple ensembles are unpacked but no longer included in template unless includeensemble is True.
- Python
Published by winedarksea over 4 years ago
autots - 0.3.12
Latest
- add MADE error metric (consider this beta, it may change)
- "endgeneration" option to modelinterrupt
- statsmodels warning adjustment (warnings now print at verbose = 2)
- add modifier to cpucount and use with modelforecast auto_model
- Python
Published by winedarksea over 4 years ago
autots - 0.3.10
Latest
- BestN ensembles now support weighting model components
- cluster-based and generatescoreper_series-based 'simple' ensembles
- 'univariate' model_list added
- similarity and custom cross validation now set initial evaluation segment
- validationtestindexes and train now include initial eval segment
- 'subsample' ensemble expansion of 'simple'
- added Theta model from statsmodels
- added ARDL model from statsmodels
- expanded UnobservedComponents functionality, although it still fails on some params for unknown reasons
- fixed bug in AutoTS.predict() where it was breaking regressors in some cases
- transition from [] to None as default for no future_regressor
- enforce more extensive failing if regression_type==User and no regressor passed
- fixed regressor handling in DatepartRegression
- Python
Published by winedarksea over 4 years ago
autots - 0.3.9
Latest
- update validation template creation for horizontal ensembles
- made MultivariateRegression probabilistic
- fixed bug where weighting didn't take floats
- pushed the evaluate options from a separate function to part of the PredictionObject
- added 'custom' validation option
- added "similarity" validation option
- SectionalMotif model added
- window functions grouped in module
- fixed bugs in holiday_flag
- holiday_flag now has holiday categorical encoding option and works better on sub-daily data
- create_regressor handle categorical features
- 'superfast' transformer_dict now adjusts fillna methods as well
- optimizing metric calculation runtimes (feel the speed of 500 µs savings!)
- Python
Published by winedarksea over 4 years ago
autots - 0.3.8
Latest
- add Transformer model to sklearn DNN models
- expanded and tuned KerasRNN model options
- added param space for RandomForest, ExtraTrees, Poisson, and RANSAC regressions
- removed Tensorflow models from UnivariateRegression as it can cause a crash with GPU training
- added create_regressor function
- two new impute methods (KNNImputer, IterativeImputerExtraTrees), but only with "all" transformers
- deletion of old TSFresh model, which was horribly slow and not going to get any faster
- optimizing scalability by tuning transformer and imputation defaults
- MultivariateRegression model (RollingRegression but 1d to models)
- fix for generatescoreper_series bug with all zeroes series
- bug fix for where horizontal ensembles failed if series_ids/column names were integers
- Python
Published by winedarksea over 4 years ago
autots - 0.3.7
Latest
- bug fix in fake_date imputation
- bug fix in Round
- make SinTrend fail if it fails on all series (may revert this)
- loadlinear and loadsine artificial datasets
- new NVAR model based on https://github.com/quantinfo/ng-rc-paper-code/
- tuning retrieve_regressor to allow it to better work with multioutput and univariate
- expand GluonTS models included
- GluonTS now works on univariate inputs
- GluonTS now works with regressors
- fixed bug where model_count wrong for mosaic ensembles
- fixed bug in VECM that meant it didn't couldn't utilize future_regressor
- Python
Published by winedarksea over 4 years ago
autots - 0.3.6
Latest
- back_forecast for forecast on training data
- Mosaic ensembles can now be used beyond training forecast_length and for shorter lengths too
- bestmodelname, bestmodelparams, and bestmodeltransformation_params AutoTS attributes now available
- mean, median, and ffill NaN now handle fully NaN series by returning 0.
- fixed bug that was causing mosaic generalization to fail if ffill/bfill handled all missing values
- STLFilter and HPFilter and convolution_filter Transformers added
- Python
Published by winedarksea over 4 years ago
autots - 0.3.5
Latest
- New Transfromer ScipyFilter
- New models Univariate and MultivariateMotif
- 'midhinge' and "weighted_mean" to AverageValueNaive
- Add passing regressors to WindowRegression and made more efficient window generation
- more plotting methods: plothorizontaltransformers
- for most -Regression type models,
model_paramsis now treated as kwargs and can accept any args for that model - ExtraTrees and RadiusRegressor to -Regression type models
- bug fix in generatescoreper_series
- 'Generation' now tracked in results table, plus plotting method for generation loss
- Python
Published by winedarksea almost 5 years ago
autots - 0.3.4
Latest
- improvements to joblib parallelized models (not copying the full df)
- additonal parameter checks
- made "auto" cpu_count even more conservative
- improved 'Score' generation. It should now be more equally weighted across metrics.
- fixed potential bug for horizontal ensemble selection if perfect forecasts were delivered
- Horizontal ensembles now chosen by combination of multiple metrics and metric_weighting (mae, rmse, spl, contour)
- re-weighted fillna probabilities for random choice
- addressed a few deprecation warnings
- new plot_horizontal() function for AutoTS to quickly visual horizontal ensembles
- Probabilistic and HDist ensembles are now deprecated (they can still be run by model_forecast but not by AutoTS class)
- new introduce_na parameter which makes series more robust to the last values being NaN in final but never in any validation
- Mosaic Ensembles! These can offer major improvements to MAE, but are also less stable than horizontal ensembles.
- Python
Published by winedarksea almost 5 years ago
autots - 0.3.3
Latest
- Fixed horizontal ensembles running in univariate cases (they are explicitly multivariate)
- 'superfast' transformer list added
- test on Mac for the first time, everything seems to work except lightgbm
- include first actual unittests (from existing test.py runs)
- slight change to random template generation to make sure all models are choosen at least once
- cleaned up PredictWitch -> model_forecast() a bit so that users can use it to run single models from parameters directly
- added loadlivedaily() example data and spruced up production_example.py
- tried in vain to make a quiet verbosity option for GluonTS
- added createlaggedregressor
- added Greykite model (additional regressors not working yet)
- fixed regressors bug in Prophet
- added a simple plot method to PredictionObject
- fix for deprecation warning in GLS
- Python
Published by winedarksea almost 5 years ago
autots - 0.3.2
Latest
- Table of Contents to Extended Tutorial/Readme.md
- Production Example
- add weights="mean"/median/min/max
- UnivariateRegression
- fix check_pickle error for ETS
- fix error in Prophet with latest version
- VisibleDeprecation warning for hidden_layers random choice in sklearn fixed
- prefill_na option added to allow quick filling of NaNs if desired (with zeroes for say, sales forecasting)
- made horizontal generalization more stable
- fixed bug in VAR where failing on data with negatives
- Python
Published by winedarksea almost 5 years ago
autots - 0.3.1
Latest
- Additional models to GluonTS
- GeneralTransformer transformation_params - now handle None or empty dict
- cleaning up of the appropriately named 'ModelMonster'
- improving MotifSimulation
- better error message for all models
- enable histgradientboost regressor, left it out before thinking it wouldn't stay experimental this long
- import_template now has slightly better
methodinput style - allow
ensembleparameter to be a list - NumericTransformer
- add .fit_transform method
- generally more options and speed improvement
- added NumericTransformer to future_regressors, should now coerce if they have different dtypes
- Python
Published by winedarksea about 5 years ago
autots - 0.3.0
Latest
- breaking change to model templates: transformers structure change
- grouping no longer used
- parameter generation for transformers allowing more possible combinations
- transformermaxdepth parameter
- Horizontal Ensembles are now much faster by only running models on the subset of series they apply to
- general starting template improved and updated to new transformer format
- change many np.random to random
- random.choices further necessitates python 3.6 or greater
- bug fix in Detrend transformer
- bug fix in SeasonalDifference transformer
- SPL bug fix when NaN in test set
- inverse_transform now fills NaN with zero for upper/lower forecasts
- expanded model_list aliases, with dedicated module
- bug fix (creating 0,0 order) and tuning of VARMAX
- Fix export_template bug
- restructuring of some lower-level function locations
- Python
Published by winedarksea over 5 years ago
autots - 0.2.8
Latest
- Round transformer to replace coerceinteger, ClipOutliers expanded, Slice to replace contextslicer
- pd.df Interpolate methods added to FillNA options, " " to "" in names, rollingmean_24
- slight improvement to printed progress messages
- transformer_list (also takes a dict of value:probability) allows adjusting which transformers are created in new generations.
- this does not apply to transformers loaded from imported templates
- Python
Published by winedarksea over 5 years ago
autots - 0.2.7
Latest
- 2x speedup in transformation runtime by removing double transformation
- joblib parallel to UnobservedComponents
- ClipOutliers transformer, Discretize Transformer, CenterLastValue - added in prep for transform template change
- bug fix on IntermittentOccurence
- minor changes to ETS, now replaces single series failure with zero fill, damped now is damped_trend
- 0.3.0 is expected to feature a breaking change to model templates in the transformation/pre-processing
- Python
Published by winedarksea over 5 years ago
autots - 0.2.6
Latest
- fix verbose > 2 error in auto_model
- use of f-strings to print some error messages. Python 3.5 may see more complicated error messages as a result.
- improved BestN (formery Best3) Ensembles, ensemble collected in dicts
- made Horizontal and BestN ensembles tolerant of a component model failure
- made Horizontal models capable of generalizing from a subset of series
- added info to model table for models that can use future_regressor
- added Datepart Regression model, sklearn regressor on time components only
- Python
Published by winedarksea over 5 years ago
autots - 0.2.3
Latest
- Added n_jobs parameters to pass through to joblib (although a joblib context manager is perhaps the best way)
- Added joblib multiprocessing to ETS, GLM, and FBProphet
- Fixed future warnings with pandas.DatetimeIndex.week and changes to statsmodels ETS
- standardized source code formatting
- Python
Published by winedarksea over 5 years ago
autots - 0.2.0
Latest:
Added Github Pages documentation
Changed default for `series_id` so it is no longer required if univariate
Changed default of `subset` to None.
Removed `weighted` parameter, now passing weights to .fit() alone is sufficient.
Fixed a bug where 'One or more series is 90% or more NaN' was printing when it shouldn't
Fixed (or more accurately, reduced) a bug where multiple initial runs were counting as validation runs.
Fixed bug where validation subsetting was behaving oddly
Fixed bug where regressor wasn't being passed to validation.
Renamed preord_ to future_ regressor.
Renamed sample datasets.
Allowed export of result_file as .pickle along with more complete object.
Added model_interrupt parameter to allow for manually skipping models when enabled.
Made serious efforts to make the code prettier with pylint, still lots to do, however...
Improved genetic recombination so optimal models should be reached more quickly
Improved Point to Probabilistic methods:
'historic_quantile' more stable quantile-based error ranges
'inferred normal' Bayesian-inspired method
Metrics:
Added Scaled Pinball Loss (SPL)
Removed upper/lower MAE
Improved ensembling with new parameter options
Recursive ensembling (ensemble of ensembles) now enabled
Validation:
Added 'seasonal' validation method
Categorical transformer improved, now tolerant to leaving bounds.
Added remove_leading_zeroes option for convenience.
Added a number of new Transformer options
Multiple new Sklearn-sourced transformers (QuantileTransformer, etc)
SinTrend
DifferencedDetrend
CumSumTransformer
PctChangeTransformer
PositiveShift Transformer
Log
IntermittentOccurrence
SeasonalDetrend
bkfilter and cffilter
DatepartRegression
Entirely changed the general transformer to add ~~three~~ four levels of transformation.
Allowed context_slicer to receive direct integer inputs
Added new 'Detrend' options to allow more sklearn linear models.
GLM
Error where it apparently won't tolerate any zeroes was compensated for.
Speed improvement.
RollingRegression
Added SVM model
Added option to tune some model parameters to sklearn
Added new feature construction parameters
Added RNNs with Keras
GluonTS:
fixed the use of context_length, added more options to that param
Dynamic Factor added uncertainty from Statsmodels Statespace
VARMAX added uncertainty from Statsmodels Statespace
New models:
SeasonalNaive model
VAR from Statsmodels (faster than VARMAX statespace)
MotifSimulation
WindowRegression
TensorflowSTS
TFPRegression
ComponentAnalysis
- Python
Published by winedarksea about 6 years ago
autots - 0.1.2
- fixed bug in choosing best model
- breaking change of MedValueNaive, moved into new AvgValueNaive
- increased speed by only saving detailed model results when ensembling
- improved handling of data with large numbers of NaN, prints warning now if NaN in any test set
- experimental option to save model results during run, allowing progress recovery if crashes
- added maxpermodel_class option to prevent one model from predominating after many iterations
- added weekly sample data (from EIA)
- added GluonTS models
- added TSFreshRegressor (albeit in a very slow version)
- Python
Published by winedarksea over 6 years ago