keras-beats 0.0.1

Lightweight installation of NBeats NN architecture for keras

KerasBeats

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An easy, accessible way to use the NBeats model architecture in Keras.

Table of Contents:

• Introduction
• Installation
• Basic Usage
• Data Prep
• KerasBeats layer
• KerasBeats as keras model

Introduction

The motivation for this project was to take the NBeats model architecture defined in the original paper here: https://arxiv.org/abs/1905.10437 and reproduce it in a widely accessible form in keras. In the past few years this model has become very popular as a timer series forecasting tool, but its implementation in keras seemed elusive, without an easy-to-use, well documented option online that'd be simple for newcomers to try. When you are looking to use a new tool for the first time, it's vital that you can do a simple install and quickly use an api syntax you are already familiar with to get started within minutes.

To that end, KerasBeats was built with the following ideas in mind:

• It should reflect the original model architecture as closely as possible.
• It should have a simple, high level architecture that allows people to get started as quickly as possible using the familar fit/predict methods that everyone is already familiar with
• It should allow you to quickly and easily use it as a keras model to take advantage of the libraries existing functionality and enable its use in existing workflows

Installation

kerasbeats can be installed with the following line:

pip install keras-beats

Basic Usage

The N-Beats model architecture assumes that you take a univariate time series and create training data that contains previous values for an observation at a particular point in time. For example, let's assume you have the following univariate time series:

# sample time series values
time_vals = [1, 2, 3, 4, 5]

If you were predicting one period ahead and wanted to use the previous two values in the time series as input, you want your data to be formatted like this:

# data formatting for N-beats
# each row represents the previous two values for the currently observed one
X = [[1, 2],
     [2, 3],
     [3, 4]]
     
y = [[3], 
     [4], 
     [5]]

The idea here is that [1, 2] were the two values that preceded 3, [2, 3] were the two that preceeded 4, and so on.

Once your input data is formatted like this then you can use kerasbeats in the following way:

from kerasbeats import NBeatsModel
mod = NBeatsModel()
mod.fit(X, y)

When you are finished fitting your model you can use the predict and evaluate methods, which are just wrappers on the original keras methods, and would work in the same way.

Data Prep

Most time series data typically comes in column format, so a little data prep is usually needed before you can feed it into kerasbeats. You can easily do this yourself, but there are some built in functions in the kerasbeats package to make this a little easier.

Univariate Time Series Data

If you have a single time series, you can use the prep_time_series function to get your data in the appropriate format. It works like this:

from kerasbeats import prep_time_series
# sample data:  a mock time series with ten values
time_vals = np.arange(10)
windows, labels = prep_time_series(lookback = 5, horizon = 1)

Once you are done with this the value of windows will be the following numpy array:

# training window of 5 values
array([[0, 1, 2, 3, 4],
       [1, 2, 3, 4, 5],
       [2, 3, 4, 5, 6],
       [3, 4, 5, 6, 7],
       [4, 5, 6, 7, 8]])

The value of labels will be the following numpy array:

# the value that followed the preceeding 5
array([[5],
       [6],
       [7],
       [8],
       [9]])

This method accepts numpy arrays, lists, and pandas Series and DataFrames as input, but they must be one column if they are not then you'll receive an error message.

The function contains two separate arguments:

• horizon: how far out into the future you want to predict. A horizon value of 1 means you are predicting one step ahead. A value of two means you are predicting two steps ahead, and so on
• lookback: what multiple of the horizon you want to use for training data. So if horizon is 1 and lookback is 5, your training window will be the previous 5 values. If horizon is 2 and lookback is 5, then your training window will be the previous 10 values.

Multivariate Time Series Data

You could conceivably use kerasbeats to learn a combination of time series jointly, assuming they shared common patterns between them.

For example, here's a simple dataset that contains two different time series in a dataframe:

import pandas as pd

df = pd.DataFrame()
df['label'] = ['a'] * 10 + ['b'] * 10
df['value'] = [i for i in range(10)] * 2

df would look like this in a jupyter notebook:

This contains two separate time series, one for value a, and another for value b. If you want to prep your data so each time series for each label is turned into its corresponding training windows and labels you can use the prep_multiple_time_series function:

from kerasbeats import prep_multiple_time_series
windows, labels = prep_multiple_time_series(df, label_col = 'label', data_col = 'value', lookback = 5, horizon = 2)

This function will perform the prep_time_series function for each unique value specified in the label_col column and then concatenate them together in the end, and you can then pass windows and labels into the NBeatsModel.

KerasBeats layer

The NBeatsModel is an abstraction over a functional keras model. You may just want to use the underlying keras primitives in your own work without the very top of the model itself.

The basic building block of kerasbeats is a custom keras layer that contains all of the N-Beats blocks stacked together. If you want access to this layer directly you can call the build_layer method:

from kerasbeats import NBeatsModel
model = NBeatsModel()
model.build_layer()

This exposes the layer attribute, which is a keras layer that can be re-used in larger, multi-faceted models if you would like.

KerasBeats as keras model

Likewise, you may want to access some underlying keras functionality that's not directly available in NBeatsModel. In particular, when you call fit using the NBeatsModel wrapper, the compile step is done for you automatically.

However, if you wanted to define your own separate loss functions, or define callbacks, you can access the fully built keras model in the following way:

nbeats = NBeatsModel()
nbeats.build_layer()
nbeats.build_model()

After these two lines, you can access the model attribute, which will give you access to the full keras model.

So if you wanted to specify a different loss function or optimizer, you could do so easily:

nbeats.model.compile(loss = 'mse',
                     optimizer = tf.keras.optimizers.RMSProp(0.001))
nbeats.model.fit(windows, labels)

Please note that if you want to use the underlying keras model directly, you should use nbeats.model.fit() and not nbeats.fit, since it will try and compile the model for you automatically after you call it.