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توضیحات

Automated Time Series Forecasting

ویژگی	مقدار
سیستم عامل	OS Independent
نام فایل	AutoTS-0.5.0
نام	AutoTS
نسخه کتابخانه	0.5.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Colin Catlin
ایمیل نویسنده	colin.catlin@syllepsis.live
آدرس صفحه اصلی	https://github.com/winedarksea/AutoTS
آدرس اینترنتی	https://pypi.org/project/AutoTS/
مجوز	MIT

# AutoTS <img src="/img/autots_1280.png" width="400" height="184" title="AutoTS Logo"> AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale. There are dozens of forecasting models usable in the `sklearn` style of `.fit()` and `.predict()`. These includes naive, statistical, machine learning, and deep learning models. Additionally, there are over 30 time series specific transforms usable in the `sklearn` style of `.fit()`, `.transform()` and `.inverse_transform()`. All of these function directly on Pandas Dataframes, without the need for conversion to proprietary objects. All models support forecasting multivariate (multiple time series) outputs and also support probabilistic (upper/lower bound) forecasts. Most models can readily scale to tens and even hundreds of thousands of input series. Many models also support passing in user-defined exogenous regressors. These models are all designed for integration in an AutoML feature search which automatically finds the best models, preprocessing, and ensembling for a given dataset through genetic algorithms. Horizontal and mosaic style ensembles are the flagship ensembling types, allowing each series to receive the most accurate possible models while still maintaining scalability. A combination of metrics and cross-validation options, the ability to apply subsets and weighting, regressor generation tools, simulation forecasting mode, event risk forecasting, live datasets, template import and export, plotting, and a collection of data shaping parameters round out the available feature set. ## Table of Contents * [Installation](https://github.com/winedarksea/AutoTS#installation) * [Basic Use](https://github.com/winedarksea/AutoTS#basic-use) * [Tips for Speed and Large Data](https://github.com/winedarksea/AutoTS#tips-for-speed-and-large-data) * Extended Tutorial [GitHub](https://github.com/winedarksea/AutoTS/blob/master/extended_tutorial.md) or [Docs](https://winedarksea.github.io/AutoTS/build/html/source/tutorial.html) * [Production Example](https://github.com/winedarksea/AutoTS/blob/master/production_example.py) ## Installation ``` pip install autots ``` This includes dependencies for basic models, but [additonal packages](https://github.com/winedarksea/AutoTS/blob/master/extended_tutorial.md#installation-and-dependency-versioning) are required for some models and methods. ## Basic Use Input data for AutoTS is expected to come in either a *long* or a *wide* format: - The *wide* format is a `pandas.DataFrame` with a `pandas.DatetimeIndex` and each column a distinct series. - The *long* format has three columns: - Date (ideally already in pandas-recognized `datetime` format) - Series ID. For a single time series, series_id can be `= None`. - Value - For *long* data, the column name for each of these is passed to `.fit()` as `date_col`, `id_col`, and `value_col`. No parameters are needed for *wide* data. Lower-level functions are only designed for `wide` style data. ```python # also load: _hourly, _monthly, _weekly, _yearly, or _live_daily from autots import AutoTS, load_daily # sample datasets can be used in either of the long or wide import shapes long = False df = load_daily(long=long) model = AutoTS( forecast_length=21, frequency='infer', prediction_interval=0.9, ensemble=None, model_list="fast", # "superfast", "default", "fast_parallel" transformer_list="fast", # "superfast", drop_most_recent=1, max_generations=4, num_validations=2, validation_method="backwards" ) model = model.fit( df, date_col='datetime' if long else None, value_col='value' if long else None, id_col='series_id' if long else None, ) prediction = model.predict() # plot a sample prediction.plot(model.df_wide_numeric, series=model.df_wide_numeric.columns[0], start_date="2019-01-01") # Print the details of the best model print(model) # point forecasts dataframe forecasts_df = prediction.forecast # upper and lower forecasts forecasts_up, forecasts_low = prediction.upper_forecast, prediction.lower_forecast # accuracy of all tried model results model_results = model.results() # and aggregated from cross validation validation_results = model.results("validation") ``` The lower-level API, in particular the large section of time series transformers in the scikit-learn style, can also be utilized independently from the AutoML framework. Check out [extended_tutorial.md](https://winedarksea.github.io/AutoTS/build/html/source/tutorial.html) for a more detailed guide to features. Also take a look at the [production_example.py](https://github.com/winedarksea/AutoTS/blob/master/production_example.py) ## Tips for Speed and Large Data: * Use appropriate model lists, especially the predefined lists: * `superfast` (simple naive models) and `fast` (more complex but still faster models, optimized for many series) * `fast_parallel` (a combination of `fast` and `parallel`) or `parallel`, given many CPU cores are available * `n_jobs` usually gets pretty close with `='auto'` but adjust as necessary for the environment * see a dict of predefined lists (some defined for internal use) with `from autots.models.model_list import model_lists` * Use the `subset` parameter when there are many similar series, `subset=100` will often generalize well for tens of thousands of similar series. * if using `subset`, passing `weights` for series will weight subset selection towards higher priority series. * if limited by RAM, it can be distributed by running multiple instances of AutoTS on different batches of data, having first imported a template pretrained as a starting point for all. * Set `model_interrupt=True` which passes over the current model when a `KeyboardInterrupt` ie `crtl+c` is pressed (although if the interrupt falls between generations it will stop the entire training). * Use the `result_file` method of `.fit()` which will save progress after each generation - helpful to save progress if a long training is being done. Use `import_results` to recover. * While Transformations are pretty fast, setting `transformer_max_depth` to a lower number (say, 2) will increase speed. Also utilize `transformer_list` == 'fast' or 'superfast'. * Check out [this example](https://github.com/winedarksea/AutoTS/discussions/76) of using AutoTS with pandas UDF. * Ensembles are obviously slower to predict because they run many models, 'distance' models 2x slower, and 'simple' models 3x-5x slower. * `ensemble='horizontal-max'` with `model_list='no_shared_fast'` can scale relatively well given many cpu cores because each model is only run on the series it is needed for. * Reducing `num_validations` and `models_to_validate` will decrease runtime but may lead to poorer model selections. * For datasets with many records, upsampling (for example, from daily to monthly frequency forecasts) can reduce training time if appropriate. * this can be done by adjusting `frequency` and `aggfunc` but is probably best done before passing data into AutoTS. * It will be faster if NaN's are already filled. If a search for optimal NaN fill method is not required, then fill any NaN with a satisfactory method before passing to class. * Set `runtime_weighting` in `metric_weighting` to a higher value. This will guide the search towards faster models, although it may come at the expense of accuracy. ## How to Contribute: * Give feedback on where you find the documentation confusing * Use AutoTS and... * Report errors and request features by adding Issues on GitHub * Posting the top model templates for your data (to help improve the starting templates) * Feel free to recommend different search grid parameters for your favorite models * And, of course, contributing to the codebase directly on GitHub. *Also known as Project CATS (Catlin's Automated Time Series) hence the logo.*

نیازمندی

مقدار	نام
>=1.14.6	numpy
>=0.25.*	pandas
>=0.10.*	statsmodels
>=0.20.*	scikit-learn
>=0.9	holidays
>=0.4.*	fbprophet
-	fredapi
>=1.4.1	mxnet
-	gluonts
-	tensorflow
-	xgboost
-	lightgbm
-	psutil
-	joblib

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl AutoTS-0.5.0:

pip install AutoTS-0.5.0.whl

نصب پکیج tar.gz AutoTS-0.5.0:

pip install AutoTS-0.5.0.tar.gz