Forecast dynamically at scale with this unique package. pip install scalecast

Last update: Jan 03, 2023

Overview

🌄 Scalecast: Dynamic Forecasting at Scale

About

This package uses a scaleable forecasting approach in Python with common scikit-learn and statsmodels, as well as Facebook Prophet, Microsoft LightGBM and LinkedIn Silverkite models, to forecast time series. Use your own regressors or load the object with its own seasonal, auto-regressive, and other regressors, or combine all of the above. All forecasting is dynamic by default so that auto-regressive terms can be used without leaking data into the test set, setting it apart from other time-series libraries. Dynamic model testing can be disabled to improve model evaluation speed. Differencing to achieve stationarity is built into the library and metrics can be compared across the time series' original level or first or second difference. This library was written to easily apply and compare many forecasts fairly across the same series.

import pandas as pd
import pandas_datareader as pdr
from scalecast import GridGenerator
from scalecast.Forecaster import Forecaster

models = ('mlr','knn','svr','xgboost','elasticnet','mlp','prophet')
df = pdr.get_data_fred('HOUSTNSA',start='2009-01-01',end='2021-06-01')
GridGenerator.get_example_grids()
f = Forecaster(y=df.HOUSTNSA,current_dates=df.index) # to initialize, specify y and current_dates (must be arrays of the same length)
f.set_test_length(12) # specify a test length for your models - do this before eda
f.generate_future_dates(24) # this will create future dates that are on the same interval as the current dates and it will also set the forecast length
f.add_ar_terms(4) # add AR terms before differencing
f.add_AR_terms((2,12)) # seasonal AR terms
f.integrate() # automatically decides if the y term and all ar terms should be differenced to make the series stationary
f.add_seasonal_regressors('month',raw=False,sincos=True) # uses pandas attributes: raw=True creates integers (default), sincos=True creates wave functions
f.add_seasonal_regressors('year')
f.add_covid19_regressor() # dates are flexible, default is from when disney world closed to when US CDC lifted mask recommendations
f.add_time_trend()
f.set_validation_length(6) # length, different than test_length, to tune the hyperparameters 
f.tune_test_forecast(models)
f.plot(order_by='LevelTestSetMAPE',level=True) # plots the forecast

Why switch to Scalecast?

Much simpler to set up than a tensorflow neural network
Extends scikit-learn regression modeling concepts to be useful for time-series forecasting
- propogates lagged y terms dynamically
- differences and undifferences series with ease to model stationary series only
Allows comparison of many different modeling concepts, including ARIMA, MLR, MLP, and Prophet so you never have to be in doubt about which model is right for your series
Your results and accuracy metrics can always be level, even if you need to difference the series to model it effectively

Installation

pip install scalecast
- installs the base package and most dependencies
pip install fbprophet
- only necessary if you plan to forecast with Facebook prophet models
- to resolve a common installation issue, see this Stack Overflow post
pip install greykite
- only necessary if you plan to forecast with LinkedIn Silverkite
If using notebook functions:
- pip install tqdm
- pip install ipython
- pip install ipywidgets
- jupyter nbextension enable --py widgetsnbextension
- if using Jupyter Lab: jupyter labextension install @jupyter-widgets/jupyterlab-manager

Documentation

Documentation
📋 Examples	Get straight to the process
➡ Towards Data Science Series	Read the 3-part series
📓 Binder Notebook	Play with an example in your browser
🛠️ Change Log	See what's changed
📚 Documentation Markdown Files	Review all high-level concepts in the library

Contribute

The following contributions are needed (contact [email protected])

Documentation moved to a proper website with better organization
Confidence intervals for all models (need to be consistently derived and able to toggle on/off or at different levels: 95%, 80%, etc.)
Error/issue reporting

Comments

ModuleNotFoundError: No module named 'src.scalecast'

Failing to install on intel macOS Monterey 12.6. Python 3.10.7 pip 22.2.2

Tried multiple versions (latest, 0.14.7, 0.13.11 all produce the same results:

Collecting scalecast Using cached SCALECAST-0.15.1.tar.gz (403 kB) Preparing metadata (setup.py) ... error error: subprocess-exited-with-error

× python setup.py egg_info did not run successfully. │ exit code: 1 ╰─> [6 lines of output] Traceback (most recent call last): File "", line 2, in File "", line 34, in File "/private/var/folders/0f/m_0sfrcn7c56zl0026b1k62c0000gq/T/pip-install-k0tc2z79/scalecast_d8f7a8ae59984787adfbedc8a557540a/setup.py", line 4, in from src.scalecast.init import version as version ModuleNotFoundError: No module named 'src.scalecast' [end of output]
bug

opened by callmegar 7
Is it possible to pickle only trained model and call it back for test and/or forecast without any training process in another .py folder?

I am trying to pickle a trained xgboost model and call it in another .py folder to only do forecast without any training process. Is it possible to do that with auto_forecast() function, if not how can I do that? (code is below)

"""### XGBoost"""

xgboost_grid = { 'max_depth': [15], 'tree_method': 'gpu_hist' } for i, f in enumerate(forecasts): print('Forecast', i) f.set_estimator('xgboost') f.ingest_grid(xgboost_grid) f.tune() f.auto_forecast()
question

opened by batuhansahincanel 5

Possible Bug: f.forecast gives Index error

Problem

This looks to be very promising module. The theta example given in the tutorial runs without error. But when I tried to implement the theta forecasting using this module for my example data, I got the index error for the validation.

How to fix the IndexError?

I have created a brand new virtual environment (venv) with python 3.9 and installed darts and scaleforecast.

Reproducible Example

import numpy as np
import pandas as pd
from scalecast.Forecaster import Forecaster

col_date = 'BillingDate'
col_val = 'TotWAC'

# data
url = "https://github.com/bhishanpdl/Shared/blob/master/data/data_scalecast/df_train.csv"
dfs = pd.read_html(url)

df_train = dfs[0].iloc[:,1:]
df_train[col_date] = pd.to_datetime(df_train[col_date])

y = df_train[col_val].to_list()
current_dates = df_train[col_date].to_list()

f = Forecaster(y=y,current_dates=current_dates)

f.set_test_length(.2)
f.generate_future_dates(90)
f.set_validation_metric('mape')

from darts.utils.utils import SeasonalityMode, TrendMode, ModelMode

theta_grid = {
    'theta':[0.5,1,1.5,2,2.5,3],
    'model_mode':[
        ModelMode.ADDITIVE,
        ModelMode.MULTIPLICATIVE
    ],
    'season_mode':[
        SeasonalityMode.MULTIPLICATIVE,
        SeasonalityMode.ADDITIVE
    ],
    'trend_mode':[
        TrendMode.EXPONENTIAL,
        TrendMode.LINEAR
    ],
}

f.set_estimator('theta')
f.ingest_grid(theta_grid)
f.cross_validate(k=3)
f.auto_forecast()

Error

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Input In [9], in <cell line: 44>()
     42 f.set_estimator('theta')
     43 f.ingest_grid(theta_grid)
---> 44 f.cross_validate(k=3)
     45 f.auto_forecast()

File \venv\py39darts\lib\site-packages\scalecast\Forecaster.py:3422, in Forecaster.cross_validate(self, k, rolling, dynamic_tuning)
   3420 self.grid = grid_evaluated.iloc[:, :-3]
   3421 self.dynamic_tuning = f2.dynamic_tuning
-> 3422 self._find_best_params(grid_evaluated)
   3423 self.grid_evaluated = grid_evaluated_cv.reset_index(drop=True)
   3424 self.grid = orig_grid

File \venv\py39darts\lib\site-packages\scalecast\Forecaster.py:3434, in Forecaster._find_best_params(self, grid_evaluated)
   3429     best_params_idx = self.grid.loc[
   3430         grid_evaluated["metric_value"]
   3431         == grid_evaluated["metric_value"].max()
   3432     ].index.to_list()[0]
   3433 else:
-> 3434     best_params_idx = self.grid.loc[
   3435         grid_evaluated["metric_value"]
   3436         == grid_evaluated["metric_value"].min()
   3437     ].index.to_list()[0]
   3438 self.best_params = {
   3439     k: v[best_params_idx]
   3440     for k, v in self.grid.to_dict(orient="series").items()
   3441 }
   3442 self.best_params = {
   3443     k: (
   3444         v
   (...)
   3452     for k, v in self.best_params.items()
   3453 }

IndexError: list index out of range

System Info

$ pip freeze
absl-py==1.2.0
aiohttp==3.8.1
aiosignal==1.2.0
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
asttokens==2.0.8
astunparse==1.6.3
async-timeout==4.0.2
attrs==22.1.0
autopep8==1.7.0
backcall==0.2.0
beautifulsoup4==4.11.1
bleach==5.0.1
cachetools==5.2.0
catboost==1.0.6
certifi==2022.6.15
cffi==1.15.1
charset-normalizer==2.1.1
cmdstanpy==1.0.5
colorama==0.4.5
convertdate==2.4.0
cycler==0.11.0
Cython==0.29.32
darts==0.21.0
debugpy==1.6.3
decorator==5.1.1
defusedxml==0.7.1
eli5==0.13.0
entrypoints==0.4
ephem==4.1.3
et-xmlfile==1.1.0
executing==0.10.0
fastjsonschema==2.16.1
flatbuffers==1.12
fonttools==4.36.0
frozenlist==1.3.1
fsspec==2022.7.1
gast==0.4.0
google-auth==2.11.0
google-auth-oauthlib==0.4.6
google-pasta==0.2.0
graphviz==0.20.1
greenlet==1.1.2
grpcio==1.47.0
h5py==3.7.0
hijri-converter==2.2.4
holidays==0.15
html5lib==1.1
idna==3.3
importlib-metadata==4.12.0
ipykernel==6.15.1
ipython==8.4.0
ipython-genutils==0.2.0
ipywidgets==8.0.1
jedi==0.18.1
Jinja2==3.1.2
joblib==1.1.0
jsonschema==4.14.0
jupyter==1.0.0
jupyter-client==7.3.4
jupyter-console==6.4.4
jupyter-contrib-core==0.4.0
jupyter-contrib-nbextensions==0.5.1
jupyter-core==4.11.1
jupyter-highlight-selected-word==0.2.0
jupyter-latex-envs==1.4.6
jupyter-nbextensions-configurator==0.5.0
jupyterlab-pygments==0.2.2
jupyterlab-widgets==3.0.2
keras==2.9.0
Keras-Preprocessing==1.1.2
kiwisolver==1.4.4
korean-lunar-calendar==0.2.1
libclang==14.0.6
lightgbm==3.3.2
llvmlite==0.39.0
LunarCalendar==0.0.9
lxml==4.9.1
Markdown==3.4.1
MarkupSafe==2.1.1
matplotlib==3.5.3
matplotlib-inline==0.1.6
mistune==2.0.4
multidict==6.0.2
nbclient==0.6.6
nbconvert==7.0.0
nbformat==5.4.0
nest-asyncio==1.5.5
nfoursid==1.0.1
notebook==6.4.12
numba==0.56.0
numpy==1.22.4
oauthlib==3.2.0
openpyxl==3.0.10
opt-einsum==3.3.0
packaging==21.3
pandas==1.4.3
pandas-datareader==0.10.0
pandocfilters==1.5.0
parso==0.8.3
patsy==0.5.2
pickleshare==0.7.5
Pillow==9.2.0
plotly==5.10.0
pmdarima==2.0.0
prometheus-client==0.14.1
prompt-toolkit==3.0.30
prophet==1.1
protobuf==3.19.4
psutil==5.9.1
pure-eval==0.2.2
pyasn1==0.4.8
pyasn1-modules==0.2.8
pycodestyle==2.9.1
pycparser==2.21
pyDeprecate==0.3.2
Pygments==2.13.0
PyMeeus==0.5.11
pyodbc==4.0.34
pyparsing==3.0.9
pyrsistent==0.18.1
python-dateutil==2.8.2
pytorch-lightning==1.7.2
pytz==2022.2.1
pywin32==304
pywinpty==2.0.7
PyYAML==6.0
pyzmq==23.2.1
qtconsole==5.3.1
QtPy==2.2.0
requests==2.28.1
requests-oauthlib==1.3.1
rsa==4.9
SCALECAST==0.13.11
scikit-learn==1.1.2
scipy==1.9.0
seaborn==0.11.2
Send2Trash==1.8.0
setuptools-git==1.2
six==1.16.0
soupsieve==2.3.2.post1
SQLAlchemy==1.4.40
stack-data==0.4.0
statsforecast==0.6.0
statsmodels==0.13.2
tabulate==0.8.10
tbats==1.1.0
tenacity==8.0.1
tensorboard==2.9.1
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorflow==2.9.1
tensorflow-estimator==2.9.0
tensorflow-io-gcs-filesystem==0.26.0
termcolor==1.1.0
terminado==0.15.0
threadpoolctl==3.1.0
tinycss2==1.1.1
toml==0.10.2
torch==1.12.1
torchmetrics==0.9.3
tornado==6.2
tqdm==4.64.0
traitlets==5.3.0
typing_extensions==4.3.0
ujson==5.4.0
urllib3==1.26.12
watermark==2.3.1
wcwidth==0.2.5
webencodings==0.5.1
Werkzeug==2.2.2
widgetsnbextension==4.0.2
wrapt==1.14.1
xarray==2022.6.0
xgboost==1.6.2
yarl==1.8.1
zipp==3.8.1

question

opened by bhishanpdl 4

Generating future values does not show in plot

Hello,

I am using scalecast for a timeseries project consisting of predicting future asset prices. I am currently deploying my model in production but I cannot generate any future values beyond my testing dataset. At the beginning of my script, I have written "f.generate_future_dates(20)" but the f.plot() method does not return the predictions for the next 20 units.

Would you please guide me for how to generate and plot these next 20 units?

Thank you.

Martin
question

opened by MartinMashalov 4
Update export() methods to include CIs more easily + at level=True

This may already be a feature, but i think some of the export methods could be retooled.

Specifically, I would like to pull the CIs for test set predictions and forecasts at the level=True.

export_forecasts_with_cis and export_test set_preds_with_cis don’t currently have a level value to call on according to the docs.

Additionally, it would be nice to be able to call these dfs in the .export() method. This way, I can more easily call the functions for a list of models.
enhancement

opened by jroy12345 4
VECM

Hi Michael,

VECM has been showing a frequency error when you have gaps in your data. I would like to know if it's possible to correct that for all type of frequency even if we have gaps in the data like other models in scalecast that works well for this cases.

Best regards,

Michelle
bug

opened by michellebaugraczyk 3
No option to save LSTM model

I trained an LSTM model and couldn't figure out a way to save my model in any format. I've been through all of the docs provided but there's no way to get the job done. Need help.
enhancement

opened by KnightLock 3
use_boxcox parameter in holt winters

with this grid:

hwes = { 'trend':['add','mul'], 'seasonal':['add','mul'], 'damped_trend':[True,False], 'initialization_method':[None,'estimated','heuristic'], 'use_boxcox':[True,False], 'seasonal_periods':[168], }

I get this result which I do not understand as it is True or False:

`File ~/.local/share/virtualenvs/scalecast-0HQw5DtN/lib/python3.10/site-packages/statsmodels/tsa/holtwinters/model.py:337, in ExponentialSmoothing._boxcox(self) 335 y = boxcox(self._y, self._use_boxcox) 336 else: --> 337 raise TypeError("use_boxcox must be True, False or a float.") 338 return y

TypeError: use_boxcox must be True, False or a float.`

I think the issue may be here: File ~/.local/share/virtualenvs/scalecast-0HQw5DtN/lib/python3.10/site-packages/statsmodels/tsa/holtwinters/model.py:291, in ExponentialSmoothing.__init__(self, endog, trend, damped_trend, seasonal, seasonal_periods, initialization_method, initial_level, initial_trend, initial_seasonal, use_boxcox, bounds, dates, freq, missing) 289 self._use_boxcox = use_boxcox 290 self._lambda = np.nan --> 291 self._y = self._boxcox() 292 self._initialize() 293 self._fixed_parameters = {}
bug

opened by callmegar 2

Issue when running auto_forecast() or tune_test_forecast() with rf

Here is the error. Typically I can rerun the codeblock in my notebook and after 1-2 tries it will fix itself.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_3112\911625238.py in <module>
      3     j.ingest_grid(model)
      4     j.cross_validate(dynamic_tuning=26)
----> 5     j.auto_forecast()
      6     j.save_summary_stats()
      7     print(model)

~\AppData\Roaming\Python\Python37\site-packages\scalecast\Forecaster.py in auto_forecast(self, call_me, dynamic_testing, test_only)

~\AppData\Roaming\Python\Python37\site-packages\scalecast\Forecaster.py in manual_forecast(self, call_me, dynamic_testing, test_only, **kwargs)

~\AppData\Roaming\Python\Python37\site-packages\scalecast\Forecaster.py in _forecast_sklearn(self, fcster, dynamic_testing, tune, Xvars, normalizer, test_only, **kwargs)

~\AppData\Roaming\Python\Python37\site-packages\scalecast\Forecaster.py in evaluate_model(scaler, regr, X, y, Xvars, fcst_horizon, future_xreg, dynamic_testing, true_forecast)

~\Anaconda3\envs\time\lib\site-packages\sklearn\ensemble\_forest.py in fit(self, X, y, sample_weight)
    465                     n_samples_bootstrap=n_samples_bootstrap,
    466                 )
--> 467                 for i, t in enumerate(trees)
    468             )
    469 

~\Anaconda3\envs\time\lib\site-packages\joblib\parallel.py in __call__(self, iterable)
   1041             # remaining jobs.
   1042             self._iterating = False
-> 1043             if self.dispatch_one_batch(iterator):
   1044                 self._iterating = self._original_iterator is not None
   1045 

~\Anaconda3\envs\time\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)
    859                 return False
    860             else:
--> 861                 self._dispatch(tasks)
    862                 return True
    863 

~\Anaconda3\envs\time\lib\site-packages\joblib\parallel.py in _dispatch(self, batch)
    777         with self._lock:
    778             job_idx = len(self._jobs)
--> 779             job = self._backend.apply_async(batch, callback=cb)
    780             # A job can complete so quickly than its callback is
    781             # called before we get here, causing self._jobs to

~\Anaconda3\envs\time\lib\site-packages\joblib\_parallel_backends.py in apply_async(self, func, callback)
    206     def apply_async(self, func, callback=None):
    207         """Schedule a func to be run"""
--> 208         result = ImmediateResult(func)
    209         if callback:
    210             callback(result)

~\Anaconda3\envs\time\lib\site-packages\joblib\_parallel_backends.py in __init__(self, batch)
    570         # Don't delay the application, to avoid keeping the input
    571         # arguments in memory
--> 572         self.results = batch()
    573 
    574     def get(self):

~\Anaconda3\envs\time\lib\site-packages\joblib\parallel.py in __call__(self)
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    262             return [func(*args, **kwargs)
--> 263                     for func, args, kwargs in self.items]
    264 
    265     def __reduce__(self):

~\Anaconda3\envs\time\lib\site-packages\joblib\parallel.py in <listcomp>(.0)
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    262             return [func(*args, **kwargs)
--> 263                     for func, args, kwargs in self.items]
    264 
    265     def __reduce__(self):

~\Anaconda3\envs\time\lib\site-packages\sklearn\utils\fixes.py in __call__(self, *args, **kwargs)
    214     def __call__(self, *args, **kwargs):
    215         with config_context(**self.config):
--> 216             return self.function(*args, **kwargs)
    217 
    218 

~\Anaconda3\envs\time\lib\site-packages\sklearn\ensemble\_forest.py in _parallel_build_trees(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)
    171 
    172         indices = _generate_sample_indices(
--> 173             tree.random_state, n_samples, n_samples_bootstrap
    174         )
    175         sample_counts = np.bincount(indices, minlength=n_samples)

~\Anaconda3\envs\time\lib\site-packages\sklearn\ensemble\_forest.py in _generate_sample_indices(random_state, n_samples, n_samples_bootstrap)
    127 
    128     random_instance = check_random_state(random_state)
--> 129     sample_indices = random_instance.randint(0, n_samples, n_samples_bootstrap)
    130 
    131     return sample_indices

mtrand.pyx in numpy.random.mtrand.RandomState.randint()

_bounded_integers.pyx in numpy.random._bounded_integers._rand_int32()

TypeError: 'numpy.float64' object cannot be interpreted as an integer

bug

opened by jroy12345 2

None doesn't work when passed to the 'Xvars' key in a grid and using cross validation

When using the cross_validate() function, it throws an error when using None as an argument:

arima_grid = {
    'order':[
        (1,0,1),
        (1,0,0),
        (0,0,1)
    ],
    'seasonal_order':[
        (1,0,1,7),
        (1,0,0,7),
        (0,0,1,7),
        (0,1,0,7)
    ],
    'Xvars':[
        None,
        [
            'monthsin',
            'monthcos',
            'quartersin',
            'quartercos',
            'dayofyearsin',
            'dayofyearcos',
            'weeksin',
            'weekcos',
        ]
    ],
}

f.ingest_grid(arima_grid)
f.cross_validate(k=3)

bug good first issue

opened by uger7 2

fbprophet is now named prophet

The prophet forecaster still uses the old 'fbprophet' dependency, this has been changed to 'prophet' and installing the previous versions of the library causes some issues if you have the new version/name installed

opened by callmegar 1

Releases(0.1.4)

0.1.4(Jul 1, 2021)

Source code(tar.gz)
Source code(zip)
0.1.3(Jul 1, 2021)

Source code(tar.gz)
Source code(zip)
0.1.2(Jul 1, 2021)

Source code(tar.gz)
Source code(zip)
0.1.1(Jul 1, 2021)

Source code(tar.gz)
Source code(zip)
0.1(Jul 1, 2021)

The first release for scalecast: a flexible, miminal-code forecasting module.
Source code(tar.gz)
Source code(zip)

Owner

Michael Keith

Data Scientist for Utah Department of Health

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