Decompose Prediction

In this section, we will demonstrate how to visualize

  • time series forecasting

  • predicted components

by using the plotting utilities that come with the Orbit package.

[1]:
%matplotlib inline

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

import orbit
from orbit.models import DLT
from orbit.diagnostics.plot import plot_predicted_data,plot_predicted_components
from orbit.utils.dataset import load_iclaims

import warnings
warnings.filterwarnings('ignore')
[2]:
print(orbit.__version__)
1.1.0dev
[3]:
# load log-transformed data
df = load_iclaims()
train_df = df[df['week'] < '2017-01-01']
test_df = df[df['week'] >= '2017-01-01']

response_col = 'claims'
date_col = 'week'
regressor_col = ['trend.unemploy', 'trend.filling', 'trend.job']

Fit a model

Here we use the DLTFull model as example.

[4]:
dlt = DLT(
    response_col=response_col,
    regressor_col=regressor_col,
    date_col=date_col,
    seasonality=52,
    prediction_percentiles=[5, 95],
)

dlt.fit(train_df)
WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.
To run all diagnostics call pystan.check_hmc_diagnostics(fit)
[4]:
<orbit.forecaster.full_bayes.FullBayesianForecaster at 0x14f59b750>

Plot Predictions

First, we do the prediction on the training data before the year 2017.

[5]:
predicted_df = dlt.predict(df=train_df, decompose=True)

_ = plot_predicted_data(train_df, predicted_df,
                        date_col=dlt.date_col, actual_col=dlt.response_col)
../_images/tutorials_decompose_prediction_9_0.png

Next, we do the predictions on the test data after the year 2017. This plot is useful to help check the overall model performance on the out-of-sample period.

[6]:
predicted_df = dlt.predict(df=test_df, decompose=True)

_ = plot_predicted_data(training_actual_df=train_df, predicted_df=predicted_df,
                        date_col=dlt.date_col, actual_col=dlt.response_col,
                        test_actual_df=test_df)
../_images/tutorials_decompose_prediction_11_0.png

Plot Predicted Components

plot_predicted_components is the utility to plot each component separately. This is useful when one wants to look into the model prediction results and inspect each component separately.

[7]:
predicted_df = dlt.predict(df=train_df, decompose=True)

_ = plot_predicted_components(predicted_df, date_col)
../_images/tutorials_decompose_prediction_14_0.png

One can use plot_components to have more componets to be plotted if they are available in the supplied predicted_df.

[8]:
_ = plot_predicted_components(predicted_df, date_col,
                              plot_components=['prediction', 'trend', 'seasonality', 'regression'])
../_images/tutorials_decompose_prediction_16_0.png
[ ]:
_ = plot_predicted_components(predicted_df, date_col,
                              plot_components=['prediction', 'trend', 'seasonality', 'regression'])