Time Series Forecasting with Neural Prophet

In this post, we will explore the powerful capabilities of Neural Prophet for time series forecasting. Neural Prophet is a Python library developed by Facebook’s Core Data Science team, which combines the simplicity of Prophet with the flexibility of neural networks.

Introduction

Time series forecasting is a crucial task in various domains, such as finance, sales, and weather prediction. Traditional methods like ARIMA and exponential smoothing have been widely used for this purpose. However, these methods often struggle with complex patterns and non-linear relationships in the data.

Neural Prophet addresses these limitations by leveraging the power of neural networks. It combines the strengths of Prophet, a popular time series forecasting library, with the flexibility of neural networks. Neural Prophet can handle various types of time series data, including trend changes, seasonality, and outliers.

Installation

To get started with Neural Prophet, you need to install it using pip:

pip install neuralprophet

Additionally, you will need to install the required dependencies, such as pandas and matplotlib.

Usage

Let’s dive into an example to understand how Neural Prophet works. Suppose we have a dataset containing the daily sales of a retail store for the past few years. Our goal is to forecast the sales for the next month.

First, we need to import the necessary libraries and load the data:

import pandas as pd
from neuralprophet import NeuralProphet

# Load the data
data = pd.read_csv('sales_data.csv')

Next, we need to preprocess the data by converting the date column to a datetime format and renaming the columns:

# Preprocess the data
data['ds'] = pd.to_datetime(data['date'])
data = data.rename(columns={'sales': 'y'})

Now, we can create an instance of NeuralProphet and fit the model to our data:

# Create and fit the model
model = NeuralProphet()
model.fit(data)

To make predictions, we can use the predict method and specify the number of future periods to forecast:

# Make predictions
future = model.make_future_dataframe(data, periods=30)
forecast = model.predict(future)

Finally, we can visualize the forecasted sales using the plot method:

# Visualize the forecast
model.plot(forecast)

Conclusion

Neural Prophet is a powerful library for time series forecasting, combining the simplicity of Prophet with the flexibility of neural networks. In this post, we explored the basic usage of Neural Prophet and demonstrated how to forecast future sales using a retail store dataset.

With its ability to handle complex patterns and non-linear relationships, Neural Prophet is a valuable tool for any data scientist or analyst working with time series data. Give it a try and unlock the full potential of your time series forecasting tasks!

Happy forecasting!

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