Looks like all the statsmodels model where warnings.simplefilter("ignore", category='ConvergenceWarning') is used are failing and generating zero forecasts

Statsmodels has added some new models, and others were never included, such as:
ARDL
UECM
ThetaModel
MarkovRegression
SVAR

some of these may make valuable additions

Hello ,

Can i create a multiseries multivariate model using wide format ? this feature is available in the long format , but not there in the wide format ? If there is no such features available then we need to do one by one ?

my data looks like this

store A - Product A (time series data) Product B (time series Data) so on
Store B - Also has similar pattern .

Also i need to add external regressor. My external regressor has more than one variable. One idea is to do a dimension reduction and take it ? Or can i provide multi dimension data for external regressor ?

Please help

No matter the value that I set in max_generations arg, the orders of VARMAX that I get are always between 0 and 2.
Is there is any way to make AutoTS tries higher orders for VARIMAX models ?
I tried various parameters with AutoTS, here is an example of how I create a model and fit it.

model = AutoTS(
    forecast_length=9,
    frequency='M',
    prediction_interval=0.9,
    model_list=["VARMAX"],
    ensemble="all",
    max_generations=100,
    num_validations=2,
    validation_method="backwards",
    random_seed=7,
    models_to_validate = 0.2,
    transformer_list="all",
    transformer_max_depth=8
)
# VARMA_train_data is a wide data set of 6 columns and 54 monthly observasions
model.fit(VARMA_train_data)

If incoming index is another datatype format or a coerce-able string, attempt to convert to DatetimeIndex

Hi. Thx for great job!

I've got NaN in prediction, while the prediction horizon started one timestamp earlier.

What I did wrong?

http://joxi.ru/82Q1PXDcVR69B2

When we call the fit() method of the AutoTS class, the console is filled with messages which most users do not need to read. It is advisable to have a straightforward way to disable these messages.

Ideally, providing a boolean parameter called verbose/silent to hide these messages should solve the problem. I would recommend defaulting it to False.

There are three ways to do this:

1. Create a new variable called silent.
2. Rename the currently used verbose variable to something else and create a new boolean variable named verbose.
3. Modify the existing verbose variable to do what we want.

trafficTest JupyterNB html.zip
Hi,

My historical data for training was from
01-04-2020 (start) to 01-10-2020 (end) as a univariate time series with website visitor traffic as the value having a daily frequency.

When I tried forecasting for a 30 day horizon by setting forecast_length=30, frequency='infer' , I was getting the forecast for December'2020 (instead of forecast horizon start from 02/10/20) . I then changed the frequency setting from 'infer' to '1D' and tried the predictions and observed the same result in the forecast.

Then, I tried setting forecast_length=5 and frequency='MS' . For this , I got the forecast starting from 1/1/2021 as shown below. November and December 2020 was missing.

2021-01-01	465546.5
2021-02-01	61375.0
2021-03-01	39539.5
2021-04-01	33329.0
2021-05-01	33581.0

--
Attached herewith is the traffic dataset and the Jupyter Notebook run output for reference.

--

traffic.zip

Simplify users having to choose a model list. Set as default.

Describe the bug
I tried to use DNN models presented in the package such as GluonTS, pytorch-forecasting and tensorflowSTS. I have installed all required packages as in the installation guide (My environment can be viewed below). The error I got from the fit() function read as:

"Evaluation Metrics are missing and all models have failed, by an error in template or metrics. There are many possible causes for this, bad parameters, environment, or an unreported bug. Usually this means you are missing required packages for the models like fbprophet or gluonts, or that the models in model_list are inappropriate for your data. A new starting template may also help".

To Reproduce
When I run the following code:

from autots import AutoTS
model_list = ['TensorflowSTS']
model = AutoTS(
    forecast_length=7,
    frequency='D',
    prediction_interval=0.9,
    metric_weighting=metric_weighting,
    ensemble='horizontal',
    holiday_country='UK',
    model_list=model_list,  
    drop_most_recent=0,
    max_generations=1,
    num_validations=3,
    validation_method="backwards",
    no_negatives=True,
    verbose=-2,
    n_jobs = 72
)

model = model.fit(train_df)

Expected behavior
Stack trace:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File ~/anaconda3/envs/forecast/lib/python3.10/site-packages/pandas/core/indexes/base.py:3621, in Index.get_loc(self, key, method, tolerance)
   3620 try:
-> 3621     return self._engine.get_loc(casted_key)
   3622 except KeyError as err:

File ~/anaconda3/envs/forecast/lib/python3.10/site-packages/pandas/_libs/index.pyx:136, in pandas._libs.index.IndexEngine.get_loc()

File ~/anaconda3/envs/forecast/lib/python3.10/site-packages/pandas/_libs/index.pyx:163, in pandas._libs.index.IndexEngine.get_loc()

File pandas/_libs/hashtable_class_helper.pxi:5198, in pandas._libs.hashtable.PyObjectHashTable.get_item()

File pandas/_libs/hashtable_class_helper.pxi:5206, in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'smape'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
File ~/anaconda3/envs/forecast/lib/python3.10/site-packages/autots/evaluator/auto_model.py:2031, in generate_score(model_results, metric_weighting, prediction_interval)
   2030 # not sure why there are negative SMAPE values, but make sure they get dealt with
-> 2031 if model_results['smape'].min() < 0:
   2032     model_results['smape'] = model_results['smape'].where(
   2033         model_results['smape'] >= 0, model_results['smape'].max()
   2034     )

File ~/anaconda3/envs/forecast/lib/python3.10/site-packages/pandas/core/frame.py:3505, in DataFrame.__getitem__(self, key)
   3504     return self._getitem_multilevel(key)
-> 3505 indexer = self.columns.get_loc(key)
   3506 if is_integer(indexer):

File ~/anaconda3/envs/forecast/lib/python3.10/site-packages/pandas/core/indexes/base.py:3623, in Index.get_loc(self, key, method, tolerance)
   3622 except KeyError as err:
-> 3623     raise KeyError(key) from err
   3624 except TypeError:
   3625     # If we have a listlike key, _check_indexing_error will raise
   3626     #  InvalidIndexError. Otherwise we fall through and re-raise
   3627     #  the TypeError.

KeyError: 'smape'

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
File <timed exec>:44, in <module>

File ~/anaconda3/envs/forecast/lib/python3.10/site-packages/autots/evaluator/auto_ts.py:791, in AutoTS.fit(self, df, date_col, value_col, id_col, future_regressor, weights, result_file, grouping_ids, validation_indexes)
    789 # capture the data from the lower level results
    790 self.initial_results = self.initial_results.concat(template_result)
--> 791 self.initial_results.model_results['Score'] = generate_score(
    792     self.initial_results.model_results,
    793     metric_weighting=metric_weighting,
    794     prediction_interval=prediction_interval,
    795 )
    796 if result_file is not None:
    797     self.initial_results.save(result_file)

File ~/anaconda3/envs/forecast/lib/python3.10/site-packages/autots/evaluator/auto_model.py:2126, in generate_score(model_results, metric_weighting, prediction_interval)
   2123         overall_score = overall_score + (containment_score * containment_weighting)
   2125 except Exception as e:
-> 2126     raise KeyError(
   2127         f"""Evaluation Metrics are missing and all models have failed, by an error in template or metrics.
   2128         There are many possible causes for this, bad parameters, environment, or an unreported bug.
   2129         Usually this means you are missing required packages for the models like fbprophet or gluonts,
   2130         or that the models in model_list are inappropriate for your data.
   2131         A new starting template may also help. {repr(e)}"""
   2132     )
   2134 return overall_score.astype(float)

Desktop (please complete the following information):

• OS: Ubuntu 20.04
• Package Versions 0.4.2

``

Name Version Build Channel

_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
abseil-cpp 20210324.2 h9c3ff4c_0 conda-forge
absl-py 1.1.0 pyhd8ed1ab_0 conda-forge
aiohttp 3.8.1 py310h5764c6d_1 conda-forge
aiosignal 1.2.0 pyhd8ed1ab_0 conda-forge
alembic 1.8.0 pyhd8ed1ab_0 conda-forge
appdirs 1.4.4 pypi_0 pypi
arch 5.3.1 pypi_0 pypi
argon2-cffi 21.3.0 pypi_0 pypi
argon2-cffi-bindings 21.2.0 pypi_0 pypi
asttokens 2.0.5 pypi_0 pypi
astunparse 1.6.3 pyhd8ed1ab_0 conda-forge
async-timeout 4.0.2 pyhd8ed1ab_0 conda-forge
attrs 21.4.0 pyhd8ed1ab_0 conda-forge
autopage 0.5.1 pyhd8ed1ab_0 conda-forge
autots 0.4.2 pyhd8ed1ab_0 conda-forge
backcall 0.2.0 pypi_0 pypi
backports 1.0 py_2 conda-forge
backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge
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blas 1.0 mkl conda-forge
bleach 5.0.0 pypi_0 pypi
blinker 1.4 py_1 conda-forge
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click 8.1.3 py310hff52083_0 conda-forge
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cudatoolkit 11.3.1 h9edb442_10 conda-forge
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zipp 3.8.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.12 h166bdaf_1 conda-forge
zstd 1.5.2 h8a70e8d_2 conda-forge
``

Add DeepVar and GPVar models to GluonTS.
These are multivariate, whereas existing GluonTS models handle data in univariate way.

From idea by @mreismendes in:
#24

1. Allow users to more easily view ongoing accuracy during metrics
2. Allow users to easily print accuracy of the chosen best model - what the accuracy was in train and validation segments
3. (potentially) add easy graphic of forecasts or a graph to quickly show max accuracy for each generation.

inspired by #61

WindowRegression (located in autots.models.sklearn) is the name here for turning a time series into a regression problem by means of taking "windows" or snapshots of data immediately preceding the forecast target, and then using them as the X/dependent variables in the model.

However, the current implementation tries to do a little too much at once. In particular this occurs with the input_dim and output_dim parameters. Given a "multivariate" input dim and a "forecast_length" output dim, this results in a regression model with an Y/target size of n_series * forecast_length. Since many regression models in turn rely on MultiOutputRegressor to predict such multivariate Y's (which generates a unique model for each Y) this quickly becomes an impossibly huge model to train.

While I think there is value in most of the current parameters, it would probably make the most sense to split this model into two separate models - basically a FastWindowRegression and a SlowWindowRegression (not called that exactly, of course).

When I try to train one of these models individually or in a group, I have the following errror:

KeyError: 'Inconceivable! Evaluation Metrics are missing and all models have failed, by an error in TemplateWizard or metrics. A new template may help, or an adjusted model_list.'

Here is a simplified version of my code, which employs random numbers instead of fixed numbers and has the same problem:

`import numpy as np
from autots import AutoTS
import pandas as pd
import datetime
from datetime import timedelta

Model configuration

model_list = [
'DynamicFactor',
'VECM',
'VAR',
'FBProphet',
]

metric_weighting = {
'smape_weighting' : 0,
'mae_weighting' : 30,
'rmse_weighting' : 5,
'containment_weighting' : 0,
'runtime_weighting' : 5,
'spl_weighting': 0,
'contour_weighting': 0,
}

model = AutoTS(
forecast_length=1,
frequency='infer',
prediction_interval=0.95,
no_negatives=False,
ensemble='simple',
constraint=0,
max_generations=300,
max_per_model_class=1,
na_tolerance=0.25,
validation_method='seasonal 14',
model_list=model_list,
models_to_validate=1,
num_validations=6,
drop_data_older_than_periods=14,
n_jobs='auto',
metric_weighting=metric_weighting,
)

Build date list for the dataframe index

InitialDate=datetime.datetime(2013,1,6)
DateFinal=datetime.datetime(2018,12,22)
DaysNumber=(DateFinal-InitialDate).days+1
Dates=list()
Date=InitialDate
for dia in range(0,DaysNumber):
Dates.insert(dia,Date)
Date=Date + timedelta(days=1)

Load time series data

DFTimeSeries=pd.DataFrame(np.random.rand(len(Dates),1))
DFTimeSeries.index=Dates

Training

model = model.fit(DFTimeSeries,result_file='pickle')
`

I am working with Python 3.8.5 64-bit with Conda

Horizontal 'small' option utilizing fewer models.
Cleanse similar models out first, before horizontal ensembling
Horizontal ensemble run/chosen on first validation (which is subset) -keep this??

Traceback (most recent call last):
File "C:/Zero/Python/时间序列比较/autots.py", line 8, in
from autots import AutoTS, load_daily
File "C:\Zero\Python\时间序列比较\autots.py", line 8, in
from autots import AutoTS, load_daily
ImportError: cannot import name 'AutoTS'

Hi man,

I can't believe I discovered the project just now! I thought I was the only one scratching my head as to why something like this does not exists. You seem to have progressed further than me, I have been busy with it just the last week or so. A few of the things I want to do you already seemed to have done. The following is what I want to work on:

AtsPy Future Development

1. Additional in-sample validation steps to stop deep learning models from over and underfitting.
1. Extra performance metrics like MAPE and MAE.
1. Improved methods to select the window lenght to use in training and calibrating the model.
1. Add the ability to accept dirty data, and have the ability to clean it up, inetrpolation etc.
1. Add a function to resample to a larger frequency for big datasets.
1. Add the ability to algorithmically select a good enough chunk of a large dataset to balance performance and time to train.
1. More internal model optimisation using AIC, BIC an AICC.
1. Code annotations for other developers to follow and improve on the work being done.
1. Force seasonality stability between in and out of sample training models.
1. Make AtsPy less dependency heavy, currently it draws on tensorflow, pytorch and mxnet.

I am going to take a small break from development for the next month to write a paper, but I look forward to learning from your project and including some tidbits of your work in my own. For what its worth, I am going to make you a collaborator for AtsPy so if you see anything out of the ordinary don't be afraid to tinker. We might be up to something slightly different, in my project I want to emphasise the "no effort" factor and give users the ability to retrieve the model and access parameters. If our projects converge then maybe we should think about collaboration.

Regards,
Derek

edit AtsPy - https://github.com/firmai/atspy

Right now, the Score (a weighted combination of the metrics) tends to favor some metrics over others. A perfect balance is impossible, but improvement is definitely possible.

Problem: A dataset with multiple data columns that may or may not be temporally coupled, and also multiple output sets.
e.g. market sectors and wellbeing index as columns, see if one index is tied to the rest of the indices.

Currently only chooses best "overall" models and should be expanded to target particular clusters or types of series within multivariate datasets, making more valuable for inclusion in horizontal ensembles.

Hello ,

Can we pickle the model ? and use it for inferencing later ?

Please suggest

Does this package support time series forecasting of intermittent or block demand?

Hi Dear. hope you all doing good i want to ask some question about some basic things like
how give train data ?
how we can give test data ?
how we can give target colum ?
As i read doc but i never i understand forecast_length what this use for?
last question is that
as i read in doc catgegorical data must label encode before to feed and is it must to use all int dtype or we can use float dtype columns as well please helpme to answere
i would realy appreciate your help.
Thanks

Very good AutoTs, can you tell me how to save the trained model for reading instead of training every time?

Hi,

You have mentioned in the document that
Probabilistic forecasts are available for all models, but in many cases are just data-based estimates in lieu of model estimates, so be careful. upper_forecasts_df = prediction.upper_forecast lower_forecasts_df = prediction.lower_forecast
Say, If I input only 'probabilistic' in the model list and run a forecast, does this statement still hold good? If not , how can we get probabilistic forecasting from probabilistic models like tensor-flow probability ? I mean , I want to get the 25th and 75th percentile forecasts from the forecast horizon distribution?

Thanks in advance.

Currently I can see only a handful features for imputation in tools.
What is the status here? Any plans to implement Kaiman filtering for example?

In line with #28, RollingRegression needs to improved for use at scale

assuming i already trained a model and now i went use it during production.
now let say my data frequently is daily and every day i am getting a new daily data and i need to predict the next day
how do i add the new daily data to my model without training it from the beginning?
can i use my trained model and giving prediction for every day?
or i have to train my model every time i am getting new daily data and then making predictions for the next day
thanks

This will effect "seasonal" and "similarity" methods

I am passing wide format...but var is throwing error , so what needs to be done for this ?

I have tried to use this library for my work but the results are not reproducible. Can you please tell why is it happening?

mosaic window, max models for horizontal, clusters/subsample count

As described. Although whether to do this only with model_forecast or with full AutoTS class (fractional validation?) is an open question

Not necessarily comprehensive yet:

• for some transformers
• import and export of templates
• generating forecasts of right size and within x % of data
• reshape of long to wide
• and more...

Hi,

long = True
# df = load_monthly(long=long)

from autots import AutoTS

metric_weighting = {
    "smape_weighting": 5,
    "mae_weighting": 1,
    "rmse_weighting": 0,
    "containment_weighting": 0,
    "runtime_weighting": 0,
    "spl_weighting": 0,
    "contour_weighting": 0,
}

model = AutoTS(
    forecast_length=30,
    frequency="infer",
    prediction_interval=0.9,
    ensemble=None,
    model_list="superfast",
    transformer_list="fast",
    max_generations=15,
    num_validations=5,
    validation_method="backwards",
    metric_weighting=metric_weighting,
)
model = model.fit(
    df,
    date_col="ds" if long else None,
    value_col="y" if long else None,
    id_col="series" if long else None,
)

prediction = model.predict()

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()
validation_results = model.results("validation")

After the run, I get the following Initiated AutoTS object with best model: AverageValueNaive, but then if I sort the validation_results dataframe by Score or smape, the first place is taken by another model, not this one.

Probably, I don't completely understand the way the best model is reported. Thank you in advance!

Hello,

for some reason I get an error that I did not get before.
I am using the same code and same data that worked before, but I still get the error.

Anyone else experiencing this issue?

Error:
SinTrend failed with IndexError('index 1 is out of bounds for axis 0 with size 0')

Kind regards
Luke

probabilities do not sum to 1 for np.random.choice on the following line -

I cannot understand how autoLS will understand which is my target. For example, passing a wide dataframe how can I understand that that y is my target and x my features for predicting the target?

How to predict time intervals in auto_ts in python

Current logo doesn't work well with dark mode.

Good question, I have no idea.
Make more scalable (multiprocessing?) if necessary, and adjust which transformer_list it is in.

Hi,

I would like to obtain the fitted values of the best model, is this possible?

allow more advanced constraint specifications

Hi,

Is your feature request related to a problem? Please describe.
It would be very nice to also have accessibility to the holidays package "subdiv" keyword, which allows to specify a country's subdivision. It can be the case, that different states have different holidays, which can influence some time series.

Describe the solution you'd like
Allow for a "holiday_subdiv" list in "create_regressor", which should not be much effort, as the functionality is offered by an already used package.

Additional context
This is my first time commenting on an issue or adding a feature request. I very much appreciate your work!

Looking forward hearing from you!