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

> Explore the awesome-python category for time series ML. Discover 17 curated libraries for forecasting, seasonality modeling, and anomaly detection.

- Repository: [Dylan Hogg/awesome-python](https://github.com/dylanhogg/awesome-python)
- Tags: guide
- Published: 2026-03-01

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**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`](https://github.com/dylanhogg/awesome-python/blob/main/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`](https://github.com/dylanhogg/awesome-python/blob/main/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`](https://github.com/dylanhogg/awesome-python/blob/main/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.

```python

# 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`](https://github.com/dylanhogg/awesome-python/blob/main/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.

```python

# 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`](https://github.com/dylanhogg/awesome-python/blob/main/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`](https://github.com/dylanhogg/awesome-python/blob/main/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`](https://github.com/dylanhogg/awesome-python/blob/main/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.