TL;DR nowcast iterates fitting sklearn (or analogous) models on time series data, with additional convenient features such as lag terms, date matching, and simulated information delays. Check out https://nowcast.readthedocs.io/en/latest/ for API usage and documentation!
Nowcasting refers to predicting in the present, short-term future, or recent past. Over the years I've re-implemented code for machine learning on time series numerous times. The basic idea is that at each prediction time, the model needs to be retrained on the most recent data available at that time. While this isn't particularly complex to perform, it can be tedious and error-prone, especially when it comes to forecasting into the future. Wouldn't it be nice to abstract this procedure away? Because of this, nowcast provides a light, modular framework for dynamic time series modeling, compatible with scikit-learn. nowcast has two main components that are interlinked.
Install from PyPI using:
pip install nowcast
It is recommended to use Python 3.6+. You can run pytest from the package root directory to check that the tests pass.
The first part, TSConfig, takes as input any number of time series data, and merges them into modeling dataframes. There are two features that make this especially useful:
- Conveniently add autoregressive (lag) terms for any variable, such as the target or any exogenous variable. Adding lag features can significantly improve predictive performance.
- Simulate information delays at the variable level. In other words, rows can be shifted so that the prediction for each timestamp uses only what information would have been available at the forecast time. Many datasets take time to compile in real time and are not available right away.
TSConfig simplifies the process of combining datasets from different domains. The data is unified into a single configuration object which is then handled directly by time series models.
The second component is AREX (AutoRegression with EXogeneity). AREX is an iterative time series predictor that abstracts away the logic of retraining a model sequentially on time series data. AREX does not impose any modeling constraints -- instead it is a procedure that can handle any model that is compatible with scikit-learn's fit/predict API.
Usually, one retrains a time series model at each time step in order to use the most recent information. The training set at each step can be either rolling (fixed size that discards old data), or expanding (use all data). Often, a time series is predicted using a combination of lags of the time series (AR), concurrent exogenous variables (EX), and lags of the exogenous variables. AREX takes care of these details for you.
On the other hand, the actual model that is applied at each time step is highly important to researchers -- it can involve preprocessing and feature engineering to using various ML algorithms and hyperparameter tuning. Thus this part is flexible and only limited by your creativity. All AREX needs is a model class with .fit() and .predict() methods, identical to sklearn. In fact, any sklearn model can be passed directly into AREX to get an
out-of-the-box time series modeler.
While the logic for time series modeling isn't particularly complicated, there are potential sources of error when one isn't careful. A simple example is using training data that shouldn't be available in forecasting.
Suppose one has annual climate data from the past century and is trying to predict global temperature 5 years ahead. On January 1, 2005 the prediction target is the entire year of 2010. Then for training we can use the (X, y) pairs from (1999->2004) and earlier, but we cannot use (2000->2005) through (2004->2009). This logic applies to every year's prediction. Otherwise the retrospective forecasts will be unfairly accurate. (Note that in some situations, (2000->2005) will be available. For example, if we make the prediction each December instead of January, we would roughly know the 2005 temperature at the time of prediction. This detail can be specified in Arex.forecast() using the t_known parameter.)
It is also absolutely possible to use only one of TSConfig or AREX for your purposes. Either readily accepts or returns their underlying pandas dataframes.
Suppose we are modeling flu incidence and our target variable is stored in the dataframe cdc. We wish to use a predictor dataframe external. First register the data:
from nowcast import TSConfig
dc = TSConfig()
dc.register_target(cdc, time_var='Timestamp', target_var='CDC')
dc.register_dataset(external, name='pred', time_var='Timestamp')Add lag terms of the target variable as autoregressive predictors:
dc.add_AR(range(1, 7), dataset='target')Suppose due to transmission dynamics we also want a lag of a predictor within the pred dataset:
dc.add_AR([1], dataset='pred', var_names=['temperature'])Call the stack method to combine the datasets. The combined dataframes (as an (X, y) tuple) can be accessed using the data property.
dc.stack()
dc.dataWe will use a default sklearn random forest as the model:
from sklearn.ensemble import RandomForestRegressor
mod = RandomForestRegressor()The above time series is at a weekly frequency. For nowcasting the present (predicting target at week t using exogenous data from week t) with a year-long rolling training window, do:
from nowcast import Arex
arex = Arex(model=mod, data_config=dc)
pred = arex.nowcast(pred_start='2019-02-19', pred_end='2019-08-20',
training='roll', window=52)Suppose we want to predict a week ahead. We would do:
pred2 = arex.forecast(t_plus=1, pred_start='2019-02-19',
pred_end='2019-08-20',
training='roll', window=52)Note that the timestamps for pred_start and pred_end refer to the time of making the prediction, not the time that is predicted.
For more instructions, please check out the docstrings in nowcast/arex.py and nowcast/data_config.py
Also included are some additional functions for working with CDC flu data. This was my original use case for nowcast. The package can be used to replicate the models of many papers, including: https://www.pnas.org/content/112/47/14473 and https://www.nature.com/articles/s41467-018-08082-0
Refer to the examples directory for a functional script with example data.