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https://github.com/tymefighter/forecast


https://github.com/tymefighter/forecast

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README 

        
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# Time Series Forecasting Package



This is a `python` package containing Various Forecasting Algorithms,

Forecasting Datasets and, Plotting, Preprocessing and Utility Tools.



## Forecasting Algorithms



This package provides (or would provide) various algorithms which work

on data containing multivariate target time series, univariate target

time series - both with or without exogenous time series. The algorithms

are provided in the `model` subpackage of the time series package `ts`.



Currently, this package provides the following Forecasting Algorithms,



-   MLP (DNN) based multivariate forecasting algorithm which has support

    for exogenous time series. You may import this in your application by

    doing as follows



```

from ts.model import DeepNN

```



-   RNN based multivariate forecasting algorithm with support for multivariate 

    exogenous time series. This is a recurrent model which takes in any

    recurrent layer (and parameters) and stacks it required number of times.



```

from ts.model import RnnForecast

```



-   Simple RNN based multivariate forecasting algorithm with support for multivariate 

    exogenous time series. This model is built by stacking multiple simple RNN layers.



```

from ts.model import SimpleRnnForecast

```



-   GRU based multivariate forecasting algorithm with support for multivariate 

    exogenous time series. This model is built by stacking multiple GRU layers.



```

from ts.model import GruForecast

```



-   LSTM based multivariate forecasting algorithm with support for multivariate 

    exogenous time series. This model is built by stacking multiple LSTM layers.



```

from ts.model import LstmForecast

```



-   The Extreme Time Model - which focuses on forecasting target series with

    extreme values - i.e. values which have a very large deviation from the

    time series trend. It supports univariate time series and multivariate 

    exogenous time series.



```

from ts.model import ExtremeTime

```



-   The Extreme Time Model 2 - It is another model focussed on forecasting

    time series with extreme values. It supports univariate time series and

    multivariate exogenous time series.



```

from ts.model import ExtremeTime2

```



-   A GMM-HMM based forecasting model which predicts by finding maximum

    posterior probability over a discrete set of observation values (since

    maximum over the contiuous observation space seems infeasible)



```

from ts.model import GmmHmmForecast

```



-   A GMM-HMM based forecasting model which predicts by finding the

    observation in the training set which is has closest log likelihood

    value to the current observation's log likelihood value



```

from ts.model import GmmHmmLikelihoodSimilarity

```



## Forecasting Data



This package provides Data Generators as well as Datasets. The `data`

subpackage of the time series package `ts` provides two subpackages

named `generate` and `dataset`, the first one contains data generators

and the second one contains real world datasets.



Currently, we provide the following,



-   ARMA Generated data - generates only univariate target series

    without support for exogenous series.



```

from ts.model.univariate.nonexo import ArmaGenerator

```



-   Standard Generators - provides generators for simple data, long

    term dependency data and extreme valued data



```

from ts.model.univariate.nonexo import StandardGenerator

```



-   Periodic Generator - generates periodic data but with support for

    only univariate target series without exogenous series.



```

from ts.model.univariate.nonexo import PeriodicGenerator

```



-   Polynomial Generator - generates data which is a polynomial function

    of time. This allows one to generate data with a varying trend.



```

from ts.model.univariate.nonexo import PolynomialGenerator

```



-   Difficult Generator - generates difficult data, i.e. data which has

    a varying trend, periodicity (seasonality) and noise (ARMA). One

    can introduce extreme values into the data by providing the appropriate

    contructor arguments



```

from ts.model.univariate.nonexo import DifficultGenerator

```



## Plotting, Preprocessing and Utility Tools



This package contains plotting tools for plotting losses, plotting training

data and comparing prediction with true (using plots). The plotting tools

are available in the `plot` subpackage of the time series package `ts`.

It also provides utility tools for in the `utility` subpackage of `ts`.

Access to the global logger and local loggers is provided in the `log`

subpackage of `ts`.



## Package Structure



```

ts/

|__ data/

|    |__ dataset/

|         |__ AmazonStockPrice

|         |__ RetailSales

|         |__ JaipurWeather

|

|    |__ generate/

|         |__ univariate/

|              |__ nonexo/

|                  |__ ArmaGenerator

|                  |__ StandardGenerator

|                  |__ PeriodicGenerator

|                  |__ PolynomialGenerator

|                  |__ DifficultGenerator

| 

|__ model/

|    |__ RnnForecast

|    |__ SimpleRnnForecast

|    |__ GruForecast

|    |__ LstmForecast

|    |__ DeepNN

|    |__ ExtremeTime

|    |__ ExtremeTime2

|    |__ GmmHmmForecast

|    |__ GmmHmmLikelihoodSimilarity

|

|__ plot/

|    |__ Plot

|

|__ log/

|    |__ ConsoleLogger

|    |__ FileLogger

|    |__ GlobalLogger

|

|__ test/

|    |__ model/..

|    |__ utility/..

|

|__ utility/

|    |__ Utility

|    |__ ForecastDataSequence

|    |__ SaveCallback

|    |__ DatasetUtility

|    |__ Metric

```



## Repository Structure



This repository is structured as follows:



```

Forecast

|__ other/...

|

|__ notebooks/..

|

|__ ts/...

```



- `other`:  contains deprecated codes and codes that do not work.

    The contents of this directory would be removed as soon as

    they have been analyzed thoroughly.



- `notebooks`: contains notebooks containing experiments, tests and examples



- `ts`: The time series forecasting package



## Existing experiments, tests and example notebooks



The `notebooks` directory of this repository contains notebooks

containing experiments, tests and examples. To be able to run

these notebooks, we need to do as follows:



1. Go to your .bashrc file and add the following line:

```

export PYTHONPATH="$PYTHONPATH:"

```



where `` is the location of this repository

in your filesystem.