Automated time-series forecasting
Project description
auto-ts
AutoTS is an easy-to-use time series forecasting model that does all the model selection work for you.
Installation
First, make sure you have installed the following packages (make sure to get the right versions when specified)
pandas
pmdarima==1.71
statsmodels==0.11.1
tbats=1.1.0
Put the AutoTS folder in your repository. Then, to use the AutoTS model in your code, import it like so:
from AutoTS.AutoTS import AutoTS
It's a lot of AutoTS's, I know
You may need to add some more to the import statement if you put the AutoTS folder inside another folder. For example, if you put it in a folder named "src" inside your repo, it might need to look more like this:
from src.AutoTS.AutoTS import AutoTS
Quickstart
AutoTS follows sci-kit learn's model.fit()
/model.predict()
paradigm. The only requirement of your
data is that it must be a pandas dataframe with a datetime index. Given such a dataframe, here is how
to train your model and make predictions:
model = AutoTS()
model.fit(data, series_column_name='passengers')
model.predict(start_date=pd.to_datetime('1960-1-1'), end_date=pd.to_datetime('1960-12-1'))
Tips/Tricks/Things to know
- Since you provide the name of the time series column during fit, the dataframe provided during fit can contain as many extra columns as you like and the model will ignore them. No need to do a bunch of filtering before training!
- You can have the model predict in-sample by setting the
start_date
equal to a date inside the data given during fit.
For a more thorough introduction, check out this example
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