Time Series Forecasting with Python

Price
$1,895.00 USD

Duration
3 Days

Delivery Methods
Virtual Instructor Led
Private Group

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Time Series Forecasting with Python Overview

Can you forecast future trends from past values? In today’s world of fast-moving data, professionals in finance, operations, and analytics need to turn raw datasets into reliable, forward-looking insights.

Time Series Forecasting with Python gives you the tools to explore, model, and forecast time series data using industry-standard Python libraries. You’ll begin by working with pandas, NumPy, and statsmodels to clean data, visualize patterns, and understand key structures like lag, seasonality, and trend.

You’ll then build forecasting models using ARIMA, SARIMA, and SARIMAX, with options for integrating exogenous variables. The course includes training on Recurrent Neural Networks for deep learning applications and wraps with Facebook Prophet—a scalable forecasting library designed for practical use.

This hands-on training is ideal for data scientists and analysts who want to perform time series forecasting in Python and create accurate forecasts for real-world business challenges.

Course Objectives

By the end of the Time Series Forecasting with Python course, you'll know how to perform time series forecasting using Python and apply statistical models, machine learning models, and deep learning techniques to forecast future values.

You’ll learn how to work with time series data, assess stationarity, evaluate lag structures, and build forecasting models like ARIMA (Autoregressive Integrated Moving Average), SARIMA, and SARIMAX. You’ll use pandas and NumPy for preprocessing and time series analysis, and apply error-trend-seasonality decomposition and moving average smoothing.

The course includes hands-on forecasting using Facebook Prophet and TensorFlow-based Recurrent Neural Networks. You'll also gain experience measuring model accuracy with RMSE, tuning hyperparameters, and using test data to evaluate predictive performance. By working with multiple models across datasets, you'll be ready to build and optimize forecasting solutions that drive confident decision-making.

Who Should Attend?

Intermediate Python developers who want to learn how to use Python to forecast time series data with a variety of methods.

Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

Learning Credits: Learning Credits can be purchased well in advance of your training date to avoid having to commit to specific courses or dates. Learning Credits allow you to secure your training budget for an entire year while eliminating the administrative headache of paying for individual classes. They can also be redeemed for a full year from the date of purchase. If you have previously purchased a Learning Credit agreement with New Horizons, you may use a portion of your agreement to pay for this class.

If you have questions about Learning Credits, please contact your Account Manager.

Course Prerequisites

Introduction to Python

Agenda

Module 1: Foundations of Time Series Forecasting

Introduction to time series forecasting and predictive analytics
Exploring time series data with pandas and NumPy
Importing and preparing datasets (import pandas as pd, import numpy as np)
Visualizing trends, seasonal patterns, and moving averages
Overview of forecasting involves and common real-world use cases

Module 2: Time Series Analysis with Statsmodels

Using statsmodels for statistical time series analysis
Decomposing time series with error-trend-seasonality models
Identifying stationary vs. non-stationary datasets
Detecting lag structures with ACF and PACF plots
Understanding model uses for autoregressive and MA processes

Module 3: Forecasting with Classical Statistical Models

Forecasting with ARIMA and SARIMA models
Developing SARIMAX models to include exogenous variables
Working with multiple variables and multiple models
Evaluating model performance with RMSE and forecast accuracy
Forecasting time series data and making data-driven decisions

Module 4: Deep Learning for Time Series Forecasting

Introduction to deep learning for forecasting
Building and training Recurrent Neural Networks using TensorFlow
Using neural networks for model training and prediction
Tuning hyperparameters and tracking model results
Predicting the future based on historical data

Module 5: Forecasting with Facebook Prophet

Overview of the Prophet library and its applications
Modeling trends and seasonality with minimal tuning
Building scalable forecasting models using Prophet
Comparing Prophet to classical methods and machine learning models
Forecast future values based on real datasets

Bonus Topics & Practice Labs

Applying random forest and other machine learning models to time series
Performing exploratory data analysis before model fitting
Using test data to validate predictions
Making accurate forecasts with real-world datasets like stock price trends
Analyzing results from statistical models, predictive tools, and neural architectures
Using linear combination techniques in advanced models

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