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https://github.com/firmai/atspy

AtsPy: Automated Time Series Models in Python (by @firmai)
https://github.com/firmai/atspy

automated finance forecasting forecasting-models python time-series time-series-analysis

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AtsPy: Automated Time Series Models in Python (by @firmai)

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# Automated Time Series Models in Python (AtsPy)



[![Downloads](https://pepy.tech/badge/atspy)](https://pepy.tech/project/atspy)



[![DOI](https://zenodo.org/badge/236661502.svg)](https://zenodo.org/badge/latestdoi/236661502)



---------



Finance Quant Machine Learning

------------------

- [ML-Quant.com](https://www.ml-quant.com/)  -  Automated Research Repository 



---------



[SSRN Report](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3580631)



Easily develop state of the art time series models to forecast univariate data series. Simply load your data and select which models you want to test. This is the largest repository of automated structural and machine learning time series models. Please get in contact if you want to contribute a model. This is a fledgling project, all advice appreciated. 



#### Install

```

pip install atspy

```



#### Automated Models



1. ```ARIMA``` - Automated ARIMA Modelling

1. ```Prophet``` - Modeling Multiple Seasonality With Linear or Non-linear Growth

1. ```HWAAS``` - Exponential Smoothing With Additive Trend and Additive Seasonality

1. ```HWAMS``` - Exponential Smoothing with Additive Trend and Multiplicative Seasonality

1. ```NBEATS``` -  Neural basis expansion analysis (now fixed at 20 Epochs)

1. ```Gluonts``` - RNN-based Model (now fixed at 20 Epochs)

1. ```TATS``` - Seasonal and Trend no Box Cox

1. ```TBAT``` - Trend and Box Cox

1. ```TBATS1``` - Trend, Seasonal (one), and Box Cox

1. ```TBATP1``` - TBATS1 but Seasonal Inference is Hardcoded by Periodicity

1. ```TBATS2``` - TBATS1 With Two Seasonal Periods



#### Why AtsPy?



1. Implements all your favourite automated time series models in a unified manner by simply running ```AutomatedModel(df)```.

1. Reduce structural model errors with 30%-50% by using LightGBM with TSFresh infused features.  

1. Automatically identify the seasonalities in your data using singular spectrum analysis, periodograms, and peak analysis.

1. Identifies and makes accessible the best model for your time series using in-sample validation methods.  

1. Combines the predictions of all these models in a simple (average) and complex (GBM) ensembles for improved performance.

1. Where appropriate models have been developed to use GPU resources to speed up the automation process.

1. Easily access all the models by using ```am.models_dict_in``` for in-sample and ```am.models_dict_out``` for out-of-sample prediction.



#### AtsPy Progress 



1. Univariate forecasting only (single column) and only monthly and daily data have been tested for suitability.  

1. More work ahead; all suggestions and criticisms appreciated, use the issues tab.

1. **Here** is a **[Google Colab](https://colab.research.google.com/drive/1WzwxUlAKg-WiEm_SleAzBIV6rs5VY_3W)** to run the package in the cloud and **[here you can run all the models](https://colab.research.google.com/drive/14QVrnVtT434s-xYcalHFlQg-o658nekv)**.



### Documentation by Example



----------

#### Load Package

```python

from atspy import AutomatedModel

```



#### Pandas DataFrame



The data requires strict preprocessing, no periods can be skipped and there cannot be any empty values. 



```python

import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/firmai/random-assets-two/master/ts/monthly-beer-australia.csv")

df.Month = pd.to_datetime(df.Month)

df = df.set_index("Month"); df

```



  

    

      

      Megaliters

    

    

      Month

      

    

  

  

    

      1956-01-01

      93.2

    

    

      1956-02-01

      96.0

    

    

      1956-03-01

      95.2

    

    

      1956-04-01

      77.1

    

    

      1956-05-01

      70.9

    

  



#### AutomatedModel



1. ```AutomatedModel``` - Returns a class instance.

1. ```forecast_insample``` - Returns an in-sample forcasted dataframe and performance.  

1. ```forecast_outsample``` - Returns an out-of-sample forcasted dataframe.

1. ```ensemble``` - Returns the results of three different forms of ensembles.

1. ```models_dict_in``` - Returns a dictionary of the fully trained in-sample models.

1. ```models_dict_out``` - Returns a dictionary of the fully trained out-of-sample models.



```python

from atspy import AutomatedModel

model_list = ["HWAMS","HWAAS","TBAT"]

am = AutomatedModel(df = df , model_list=model_list,forecast_len=20 )

```



Other models to try, **add as many as you like**; note ```ARIMA``` is slow: ```["ARIMA","Gluonts","Prophet","NBEATS", "TATS", "TBATS1", "TBATP1", "TBATS2"]```



#### In-Sample Performance

```python

forecast_in, performance = am.forecast_insample(); forecast_in

```



  

    

      

      Target

      HWAMS

      HWAAS

      TBAT

    

    

      Date

      

      

      

      

    

  

  

    

      1985-10-01

      181.6

      161.962148

      162.391653

      148.410071

    

    

      1985-11-01

      182.0

      174.688055

      173.191756

      147.999237

    

    

      1985-12-01

      190.0

      189.728744

      187.649575

      147.589541

    

    

      1986-01-01

      161.2

      155.077205

      154.817215

      147.180980

    

    

      1986-02-01

      155.5

      148.054292

      147.477692

      146.773549

    

  



```python

performance

```



  

    

      

      Target

      HWAMS

      HWAAS

      TBAT

    

  

  

    

      rmse

      0.000000

      17.599400

      18.993827

      36.538009

    

    

      mse

      0.000000

      309.738878

      360.765452

      1335.026136

    

    

      mean

      155.293277

      142.399639

      140.577496

      126.590412

    

  



#### Out-of-Sample Forecast



```python

forecast_out = am.forecast_outsample(); forecast_out

```



  

    

      

      HWAMS

      HWAAS

      TBAT

    

    

      Date

      

      

      

    

  

  

    

      1995-09-01

      137.518755

      137.133938

      142.906275

    

    

      1995-10-01

      164.136220

      165.079612

      142.865575

    

    

      1995-11-01

      178.671684

      180.009560

      142.827110

    

    

      1995-12-01

      184.175954

      185.715043

      142.790757

    

    

      1996-01-01

      147.166448

      147.440026

      142.756399

    

  



#### Ensemble and Model Validation Performance



```python

all_ensemble_in, all_ensemble_out, all_performance = am.ensemble(forecast_in, forecast_out)

```



```python

all_performance

```



  

    

      

      rmse

      mse

      mean

    

  

  

    

      ensemble_lgb__X__HWAMS

      9.697588

      94.043213

      146.719412

    

    

      ensemble_lgb__X__HWAMS__X__HWAMS_HWAAS__X__ensemble_ts__X__HWAAS

      9.875212

      97.519817

      145.250837

    

    

      ensemble_lgb__X__HWAMS__X__HWAMS_HWAAS

      11.127326

      123.817378

      142.994374

    

    

      ensemble_lgb

      12.748526

      162.524907

      156.487208

    

    

      ensemble_lgb__X__HWAMS__X__HWAMS_HWAAS__X__ensemble_ts__X__HWAAS__X__HWAMS_HWAAS_TBAT__X__TBAT

      14.589155

      212.843442

      138.615567

    

    

      HWAMS

      15.567905

      242.359663

      136.951615

    

    

      HWAMS_HWAAS

      16.651370

      277.268110

      135.544299

    

    

      ensemble_ts

      17.255107

      297.738716

      163.134079

    

    

      HWAAS

      17.804066

      316.984751

      134.136983

    

    

      HWAMS_HWAAS_TBAT

      23.358758

      545.631579

      128.785846

    

    

      TBAT

      39.003864

      1521.301380

      115.268940

    

  



#### Best Performing In-sample



```python

all_ensemble_in[["Target","ensemble_lgb__X__HWAMS","HWAMS","HWAAS"]].plot()

```

![png](atspy_files/insample.png)



#### Future Predictions All Models



```python

all_ensemble_out[["ensemble_lgb__X__HWAMS","HWAMS","HWAAS"]].plot()

```

![png](atspy_files/outsample.png)



#### And Finally Grab the Models



```

am.models_dict_in

```



    {'HWAAS': ,

     'HWAMS': ,

     'TBAT': }



```

am.models_dict_out

```



    {'HWAAS': ,

     'HWAMS': ,

     'TBAT': }



Follow [this link](https://colab.research.google.com/drive/1WzwxUlAKg-WiEm_SleAzBIV6rs5VY_3W) if you want to run the package in the cloud. 



#### 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 length to use in training and calibrating the model. 

1. Add the ability to accept dirty data, and have the ability to clean it up, interpolation 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. 



## Citations



If you use AtsPy in your research, please consider citing it. I have also written a [small report](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3580631) that can be found on SSRN. 



BibTeX entry:



```

@software{atspy,

  title = {{AtsPy}: Automated Time Series Models in Python.},

  author = {Snow, Derek},

  url = {https://github.com/firmai/atspy/},

  version = {1.15},

  date = {2020-02-17},

}

```



```

@misc{atspy,

  author = {Snow, Derek},

  title = {{AtsPy}: Automated Time Series Models in Python (1.15).},

  year  = {2020},

  url   = {https://github.com/firmai/atspy/},

}

```