Time Series Forecasting With Statsmodels And Prophet

Easy forecast model development with the popular time series Python packages. Time series is a unique dataset within the data science field. The data is recorded on time-frequency (e.g., daily, weekly, monthly, etc.), and each observation is related to the other. The time series data is valuable when you want to analyze what happens to your data over time and create future predictions. Time series forecasting is a method to create future predictions based on historical time series data. There are many statistical methods for time series forecasting, such as ARIMA or Exponential Smoothing.

Time series forecasting is often encountered in the business, so it’s beneficial for the data scientist to know how to develop a time series model. In this article, we will learn how to forecast time series using two popular forecastings Python packages; statsmodels and Prophet. Let’s get into it. The statsmodels Python package is an open-source package offering various statistical models, including the time series forecasting model. Let’s try out the package with an example dataset. This article will use the Digital Currency Time Series data from Kaggle (CC0: Public Domain).

Master time series forecasting with Prophet and Statsmodels. Complete guide covering implementation, evaluation, and deployment strategies for robust predictions. I’ve been thinking a lot about time series forecasting lately because I’ve seen how many businesses struggle to make accurate predictions. Whether it’s retail sales, stock prices, or website traffic, getting forecasts right can make or break decisions. That’s why I want to share my approach using two powerful tools that have transformed how I work with time series data. Time series forecasting isn’t just about predicting the future—it’s about understanding patterns in your data.

Have you ever looked at your business metrics and noticed they follow certain rhythms? That’s what we’re going to capture and use to our advantage. Let me start by showing you how to set up your environment. I always begin with clean, organized data because garbage in means garbage out. Here’s how I typically prepare my workspace: Notice how I’m creating a simple dataset with both trend and seasonal components?

This mimics real-world data where values tend to grow over time while showing regular patterns. Time series forecasting can be challenging as there are many different methods you could use and many different hyperparameters for each method. The Prophet library is an open-source library designed for making forecasts for univariate time series datasets. It is easy to use and designed to automatically find a good set of hyperparameters for the model in an effort to make skillful forecasts for data with trends and seasonal structure by default. In this tutorial, you will discover how to use the Facebook Prophet library for time series forecasting. After completing this tutorial, you will know:

Time Series Forecasting With Prophet in PythonPhoto by Rinaldo Wurglitsch, some rights reserved. In 2025, as businesses grapple with unprecedented data volumes from IoT sensors and global supply chains, accurate time-series forecasting has become the linchpin of strategic decision-making—imagine predicting stockouts before they cripple e-commerce giants or... With machine learning advancements like Prophet and Statsmodels Python libraries at the forefront, developers are unlocking predictive power that blends statistical rigor with intuitive automation, transforming raw temporal data into actionable insights for AI-driven... This decomposes the time series into interpretable components, making it ideal for business forecasting where trends and cycles dominate. ARIMA(p,d,q) models capture autocorrelation in residuals, essential for stationary series. Φ_P(B^s) φ_p(B) (1 - B^s)^D (1 - B)^d y_t = Θ_Q(B^s) θ_q(B) ε_t

Extends ARIMA to handle recurring patterns like weekly sales cycles. Time Series Forecasting with statsmodels and Prophet project will teach you essential skills for handling time series data, performing data analysis, and forecasting. We will start by training data on the ARIMA forecasting model and performing a model evaluation. There was an error while loading. Please reload this page. Time Series Forecasting with statsmodels and Prophet project will teach you essential skills for handling time series data, performing data analysis, and forecasting.

We will start by training data on the ARIMA forecasting model and performing a model evaluation. There was an error while loading. Please reload this page. Time series forecasting is an essential aspect of data analysis, widely used in various domains such as finance, economics, and supply chain management. Its objective is to predict future values based on previously observed values. In this blog post, we will explore how to use Prophet, an open-source forecasting tool developed by Facebook, specifically designed to handle time series data that exhibit seasonal trends.

Prophet is particularly useful for practitioners who may not be specialists in time series analysis but need to make forecasts. With its intuitive interface and capability to work with missing data and outliers well, Prophet provides a practical solution for many forecasting challenges. This guide will cover: Time series data consist of observations collected sequentially over time, which can display patterns such as trend (long-term movement), seasonality (regular variation), and cycles (irregular patterns). The aim of time series forecasting is to learn this underlying structure to predict future values accurately. Traditional time series forecasting methods include ARIMA (AutoRegressive Integrated Moving Average) and Exponential Smoothing.

These methods have their advantages and limitations. Prophet, however, is designed to be more intuitive and flexible, especially for data with seasonal impacts or missing values. To start using Prophet, you need to install it. Prophet is designed to work with Python, so make sure you have Python 3.6 or later installed on your machine. You can install Prophet via pip. Execute the following command in your terminal or command prompt:

Time series analysis is a crucial tool in extracting valuable insights and making predictions based on temporal data. Python offers several specialized libraries that make time series analysis easier and more efficient. In this article, we will explore two popular libraries: statsmodels and Prophet. statsmodels is a powerful library in Python for estimating and analyzing statistical models, including time series analysis. It provides a wide range of features for exploring, modeling, and forecasting time series data. Analyzing time series data with statsmodels involves several steps:

statsmodels provides a comprehensive set of tools for each of these steps, making time series analysis more accessible to beginners and experts alike. Prophet is an open-source time series forecasting library developed by Facebook's Core Data Science team. It is designed to simplify the forecasting process and provide high-quality predictions even with minimal configuration. Prophet is a package available for R and Python that aims to facilitate the process of univariate time series modeling for those who don’t have a statistical background but have knowledge from the data... The model fitting can be done with just one line, but all the parameters can be tuned for better suitability. This text aims to present the mathematical background of the packages and give soma practical examples.

The Prophet package (S. J. Taylor and Letham 2018) assumes the data generating process of a time series \(y\) can be decomposed as: \[y(t) = g(t) + s(t) + h(t) +\varepsilon_t,\] where \(g(t)\) models non-seasonal time trend, \(s(t)\) is the periodic effects in data, \(h(t)\) models the impact of holidays, and \(\varepsilon_t\) is a noise term. In the following sections we describe in detail each model. For trend modeling, the authors propose two models that cover many of the company’s applications.

The simpler is a linear regression against time:

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