Pros of Prophet

• More established and widely adopted in industry
• Extensive documentation and community support
• Handles seasonality and holidays out-of-the-box

Cons of Prophet

• Less flexible for custom model architectures
• May struggle with complex, non-linear patterns
• Limited ability to incorporate external regressors

Code Comparison

Prophet:

from fbprophet import Prophet

model = Prophet()
model.fit(df)
future = model.make_future_dataframe(periods=365)
forecast = model.predict(future)

Neural Prophet:

from neuralprophet import NeuralProphet

model = NeuralProphet()
model.fit(df, freq="D")
future = model.make_future_dataframe(df, periods=365)
forecast = model.predict(future)

Both libraries offer similar high-level APIs for time series forecasting. However, Neural Prophet provides more flexibility in model architecture and allows for easier integration of external regressors. Prophet, on the other hand, offers simpler out-of-the-box functionality for handling seasonality and holidays.

Neural Prophet builds upon Prophet's concepts while leveraging PyTorch for neural network-based forecasting. This approach enables it to capture more complex patterns and non-linear relationships in the data. However, Prophet remains a solid choice for simpler forecasting tasks and benefits from its mature ecosystem and extensive documentation.

Pros of Orbit

• More flexible modeling approach, supporting various time series patterns
• Better handling of multiple seasonalities and complex trends
• Robust uncertainty quantification through Bayesian inference

Cons of Orbit

• Steeper learning curve due to more complex API
• Slower training and inference times compared to Neural Prophet
• Less focus on interpretability of model components

Code Comparison

Neural Prophet:

from neuralprophet import NeuralProphet
m = NeuralProphet()
m.fit(df)
future = m.make_future_dataframe(df, periods=30)
forecast = m.predict(future)

Orbit:

from orbit.models import DLT
model = DLT(response_col='y', date_col='ds', seasonality=[52])
model.fit(df)
predicted = model.predict(df)

Summary

Both Neural Prophet and Orbit are powerful time series forecasting libraries, but they cater to different use cases. Neural Prophet offers a more user-friendly interface and faster performance, making it suitable for quick prototyping and simpler forecasting tasks. Orbit, on the other hand, provides more flexibility and advanced modeling capabilities, making it ideal for complex time series problems that require fine-grained control over model components and uncertainty estimation.

Pros of Greykite

• More comprehensive forecasting toolkit with multiple models and algorithms
• Offers advanced features like automatic model selection and hyperparameter tuning
• Provides extensive documentation and tutorials for ease of use

Cons of Greykite

• Steeper learning curve due to its more complex architecture
• May be overkill for simpler forecasting tasks
• Less focus on neural network-based approaches compared to NeuralProphet

Code Comparison

NeuralProphet:

from neuralprophet import NeuralProphet
m = NeuralProphet()
metrics = m.fit(df)
future = m.make_future_dataframe(df, periods=30)
forecast = m.predict(future)

Greykite:

from greykite.framework.templates.autogen.forecast_config import ForecastConfig
from greykite.framework.templates.forecaster import Forecaster
config = ForecastConfig(model_template="SILVERKITE")
forecaster = Forecaster()
result = forecaster.run_forecast_config(df, config)

Both libraries offer straightforward APIs for time series forecasting, but Greykite provides more configuration options and flexibility in model selection. NeuralProphet focuses on neural network-based forecasting, while Greykite offers a broader range of traditional and advanced forecasting techniques.

Pros of sktime

• Broader scope: Supports various time series tasks beyond forecasting, including classification and clustering
• Extensive algorithm collection: Offers a wide range of time series algorithms and models
• Scikit-learn compatible: Integrates well with the scikit-learn ecosystem

Cons of sktime

• Steeper learning curve: More complex API due to its broader scope
• Less specialized for forecasting: May not have as many forecasting-specific features as Neural Prophet

Code Comparison

sktime:

from sktime.forecasting.arima import ARIMA
from sktime.datasets import load_airline

y = load_airline()
forecaster = ARIMA(order=(1, 1, 1), seasonal_order=(1, 1, 1, 12))
forecaster.fit(y)
y_pred = forecaster.predict(fh=[1, 2, 3])

Neural Prophet:

from neuralprophet import NeuralProphet
import pandas as pd

df = pd.read_csv('your_data.csv')
m = NeuralProphet()
metrics = m.fit(df, freq="D")
future = m.make_future_dataframe(df, periods=30)
forecast = m.predict(future)

Pros of neuralforecast

• Supports a wider range of neural forecasting models, including N-BEATS, DeepAR, and TFT
• Offers more advanced features like probabilistic forecasting and hierarchical reconciliation
• Provides better integration with popular data science libraries like pandas and scikit-learn

Cons of neuralforecast

• Steeper learning curve due to more complex API and advanced features
• May require more computational resources for training and inference
• Less focused on interpretability compared to neural_prophet

Code Comparison

neural_prophet:

from neuralprophet import NeuralProphet
m = NeuralProphet()
m.fit(df)
future = m.make_future_dataframe(periods=30)
forecast = m.predict(future)

neuralforecast:

from neuralforecast import NeuralForecast
from neuralforecast.models import NBEATS
model = NeuralForecast(models=[NBEATS(input_size=7, h=1)])
model.fit(df)
forecast = model.predict()

Both libraries offer user-friendly APIs for time series forecasting, but neuralforecast provides more flexibility and advanced features at the cost of increased complexity. neural_prophet focuses on simplicity and interpretability, making it easier for beginners to get started with neural forecasting.