Machine Learning - Time Series Plots¶
General¶
In this example, we aim to present several regression plots, including linear regression, tree-based algorithms, and various residual plots. It’s important to note that these plots are purely illustrative and are based on generated data. To make the data more realistically representative, we introduce some noise, resulting in an approximately linear relationship.
Let’s begin by importing vastorbit.
import vastorbit as vo
Let’s use the airline passengers’ dataset for this example.
import vastorbit.datasets as vod
data = vod.load_airline_passengers()
Let’s proceed by creating an AR model to fit the time-series data.
# Importing the VAST ML module
from vastorbit.machine_learning.vast.tsa import AR
# Defining the Models
model = AR(p = 20)
# Fitting the models
model.fit(data, "date", "passengers")
In the context of data visualization, we have the flexibility to harness multiple plotting libraries to craft a wide range of graphical representations. vastorbit, as a versatile tool, provides support for several graphic libraries, such as Matplotlib and Plotly. Each of these libraries offers unique features and capabilities, allowing us to choose the most suitable one for our specific data visualization needs.
Note
To select the desired plotting library, we simply need to use the set_option() function. vastorbit offers the flexibility to smoothly transition between different plotting libraries. In instances where a particular graphic is not supported by the chosen library or is not supported within the vastorbit framework, the tool will automatically generate a warning and then switch to an alternative library where the graphic can be created.
Please click on the tabs to view the various graphics generated by the different plotting libraries.
We can switch to using the plotly module.
vo.set_option("plotting_lib", "plotly")
model.plot(data, "date", "passengers", npredictions = 11, method="forecast")
We can switch to using the matplotlib module.
vo.set_option("plotting_lib", "matplotlib")
model.plot(data, "date", "passengers", npredictions = 11, method="forecast")
Chart Customization¶
vastorbit empowers users with a high degree of flexibility when it comes to tailoring the visual aspects of their plots. This customization extends to essential elements such as color schemes, text labels, and plot sizes, as well as a wide range of other attributes that can be fine-tuned to align with specific design preferences and analytical requirements. Whether you want to make your visualizations more visually appealing or need to convey specific insights with precision, vastorbit’s customization options enable you to craft graphics that suit your exact needs.
Important
Different customization parameters are available for Plotly and Matplotlib. For a comprehensive list of customization features, please consult the documentation of the respective libraries: plotly, matplotlib.
Colors¶
Custom colors
fig = model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"])
Custom colors
model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"])
Size¶
Custom Width and Height
model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"], width = 450, height = 450)
Custom Width and Height
model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"], width = 6, height = 4)
Text¶
Custom Title
model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"], title_text ="Custom Title")
Custom Axis Titles
model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"], yaxis_title = "Custom Y-Axis Title")
Custom Title Text
model.plot(data, "date", "passengers", npredictions = 11, method="forecast").set_title("Custom Title")
Custom Axis Titles
model.plot(data, "date", "passengers", npredictions = 11, method="forecast").set_ylabel("Custom Y Axis")