Awesome-Python Time Series ML Category: Machine Learning – Time Series Guide

The Machine Learning – Time Series section in the dylanhogg/awesome-python repository is the dedicated awesome-python category for time series machine learning, containing 17 curated libraries specializing in forecasting, seasonality modeling, and anomaly detection.

The awesome-python list maintained by Dylan Hogg organizes thousands of Python libraries into logical categories. For data scientists working with temporal data, the Machine Learning – Time Series section provides essential tools ranging from classical statistical models to modern deep-learning frameworks.

Locating the Time Series ML Category in Awesome-Python

In the README.md file of the dylanhogg/awesome-python repository, the time series ML category appears under the hierarchical heading "Machine Learning – Time Series". This section specifically aggregates libraries that handle forecasting, econometrics, and temporal pattern recognition, distinguishing them from general-purpose machine learning tools found in other sections.

The category contains 17 curated repositories as of the latest version, each vetted for relevance to time-series analysis tasks. You can navigate directly to this section via the repository's table of contents or by searching for the "Machine Learning – Time Series" anchor in README.md.

Featured Libraries and Code Examples

The Machine Learning – Time Series section includes both statistical and deep-learning approaches. Below are minimal examples demonstrating two prominent libraries listed in this category.

Forecasting with Kats

Kats (Kits to Analyze Time Series) appears in the awesome-python list at line 4917 of README.md, described as a general-purpose time series analysis library from Facebook. It provides a unified interface for forecasting, anomaly detection, and feature extraction.

# Install: pip install kats

from kats.consts import TimeSeriesData
from kats.models.prophet import ProphetModel, ProphetParams

# Sample data

import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv")
ts = TimeSeriesData(df)

# Fit Prophet (via Kats wrapper)

params = ProphetParams()
model = ProphetModel(ts, params)
model.fit()
forecast = model.predict(steps=30)

print(forecast.head())

This example demonstrates Kats' wrapper around Prophet, enabling quick forecasting with standardized parameters. The library's inclusion in the awesome-python time series category reflects its comprehensive approach to temporal analysis workflows.

Deep Learning with NeuralProphet

NeuralProphet extends Facebook's Prophet with PyTorch-based neural network components, listed at line 4958 in README.md under "Deep learning PyTorch library for time series forecasting." It improves accuracy on complex non-linear patterns while maintaining Prophet's intuitive interface.

# Install: pip install neuralprophet

from neuralprophet import NeuralProphet
import pandas as pd

# Load a time‑series CSV with a "ds" (date) and "y" (value) column

df = pd.read_csv("https://raw.githubusercontent.com/ourownstory/neural_prophet/main/example.csv")

m = NeuralProphet()
metrics = m.fit(df, freq="D")
future = m.make_future_dataframe(df, periods=30)
forecast = m.predict(future)

print(forecast[['ds', 'yhat1']].tail())

NeuralProphet's inclusion in the Machine Learning – Time Series category highlights the section's coverage of modern deep-learning approaches alongside traditional statistical methods.

Repository Structure and Navigation

The awesome-python repository organizes these resources in specific files:

• README.md — Contains the complete catalogue including the Machine Learning – Time Series section at the "Machine Learning – Time Series" heading
• LICENSE — MIT license governing the list's content and reuse

These files define the structure of the awesome-python collection and provide direct navigation to the time-series ML category.

Summary

• The Machine Learning – Time Series section is the dedicated awesome-python category for time series ML libraries
• This section contains 17 curated repositories covering forecasting, anomaly detection, and econometrics
• Located in README.md under the "Machine Learning – Time Series" heading in the dylanhogg/awesome-python repository
• Features both classical statistical tools (like Kats) and deep-learning frameworks (like NeuralProphet)
• Provides resources for both univariate and multivariate time series analysis

Frequently Asked Questions

Where exactly is the time series category located in the awesome-python repository?

The time series machine learning category is located in the README.md file under the heading "Machine Learning – Time Series". This section specifically catalogs libraries designed for temporal data analysis, distinct from general machine learning or data manipulation sections.

How many libraries are listed in the awesome-python time series ML category?

The Machine Learning – Time Series section contains 17 curated libraries as implemented in the dylanhogg/awesome-python repository. These range from statistical forecasting tools like Prophet and Kats to deep-learning frameworks like NeuralProphet and PyTorch Forecasting.

What types of time series tasks do these libraries cover?

The libraries in this category cover forecasting, anomaly detection, seasonality decomposition, econometrics, and temporal similarity analysis. According to the source code analysis, the selection includes both classical statistical approaches and modern neural network architectures for time series data.

Is there a specific license for the awesome-python list itself?

Yes, the awesome-python repository is governed by an MIT License found in the LICENSE file at the repository root. This allows free reuse and modification of the curated list while maintaining attribution to the original dylanhogg/awesome-python project.

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