Time Series Analysis and Forecasting with Python

Name: Time Series Analysis and Forecasting with Python
Rating: 4.4 (540 reviews)

Learn Python for Pandas, Statsmodels, ARIMA, SARIMAX, Deep Learning, LSTM and Forecasting into Future

Created byNavid Shirzadi, PhD, P.Eng.

Last updated 6/2026

English

English [Auto],Spanish [Auto],

What you'll learn

Basic Packages, NumPy, Pandas & Matplotlib
Time Series with Pandas (Creating Date Time index, Resampling, ...)
Analyzing Time Series Data Using Statsmodels Package
The Concept of ARIMA and SARIMAX method and How to Forecast into the Future Using Them
The Concept of Deep Learning from A-Z
Forecast into the Future Using LSTM Model for Single Variant
Forecast into the Future Using LSTM Model for Multi Variant

Course content

6 sections • 52 lectures • 10h 18m total length

Course Content4:24
Python IDE Installation 11:50
Python IDE Installation 25:01
Install Python first and then install Spyder via pip, ensuring pip is recognized in the command prompt or terminal. Open Spyder to begin exploring its environment for time series work.
Python IDE Installation 36:00
Explore the Spyder IDE interface, configure the Python interpreter and pip-based setup, customize appearance and keyboard shortcuts, and prep your environment for time series analysis in Python.
Installing Required Libraries6:39
Install the required libraries for time series analysis with python using pip, including numpy, pandas, matplotlib, seaborn, scikit-learn, statsmodels, pmdarima, keras, and tensorflow.

Notice!!1:05
Source Codes0:13
NumPy 17:24
NumPy 29:11
NumPy 312:22
Learn element-wise addition and subtraction of NumPy arrays using operators and np.subtract, then explore broadcasting when adding scalars or differently shaped arrays, and use np.sum to total elements.
NumPy 46:32
NumPy 518:06
NumPy 616:44
Review mean, variance, standard deviation, and median in numpy, compute them for one- and two-dimensional arrays with axis, explore normal and uniform distributions, and preview pandas.
Pandas 115:39
Pandas 215:11
Pandas 314:44
Pandas 424:19
Matplotlib 114:56
Matplotlib 222:13
Matplotlib 318:53
Learn to create scatter plots, histograms, and bar charts with matplotlib, customizing color, labels, titles, grid, and legends for clear data visualization.
Matplotlib 416:02
Matplotlib 512:56
Learn how to visualize box plots with Matplotlib, customize notched and rectangular forms, and set colors for box, whiskers, caps, and median to aid outlier detection.

Introduction to ARIMA20:38
ARIMA Model Development 118:08
Import key libraries, prepare a date-time indexed dataset, and develop the first ARIMA model on temperature data, using seasonal decomposition to inspect trend and seasonality.
ARIMA Model Development 214:24
Explore arima model development by using grid search with auto_arima to determine p, d, and q, compare models by aic, and confirm the best arima configuration for series forecasting.
ARIMA Model Development 316:43
Develop an arima model with order (1,1,2) on the training data, fit it, and generate level predictions. Compare forecasts to test data with matplotlib visuals and rmse.
Introduction to SARIMAX4:25
Explore how the SARIMAX model extends ARIMA with seasonality and exogenous variables, define PDQ and seasonal length, and use grid search guided by decomposition.
SARIMAX Model Development 111:39
SARIMAX Model Development 210:33
Learn how to develop a sarimax model with exogenous variables using auto.arima, determine seasonality with seasonal_decompose, and set m=7 to forecast temperature data.
SARIMAX Model Development 314:39

Introduction to Deep Learning - Basic Concepts6:17
Introduction to Deep Learning - Activation Function7:17
Introduction to Deep Learning - How Neural Network Learn?7:09
Examine how a practical neural network learns: from input and hidden layers with selective connections, through the output's predicted value and backpropagation to minimize the cost function across all data.
Introduction to Deep Learning - Optimization9:03
Introduction to Deep Learning - Recurrent Neural Network6:01
Discover recurrent neural networks for time series analysis, featuring memory through hidden-layer feedback and back propagation through time (Bptt). Compare RNNs with feedforward nets and explore challenges.
Introduction to Deep Learning - LSTM Method12:53
Development of Univariate LSTM Model 116:02
Develop a univariate LSTM workflow by loading solar radiation data, cleaning missing values, splitting training and test sets, and scaling with a min-max scaler to 0-1.
Development of Univariate LSTM Model 26:55
Development of Univariate LSTM Model 311:51
Development of Univariate LSTM Model 432:02
Develop a univariate time series LSTM in Keras, building a four hidden layer network with dropout and a dense output, trained with Adam optimizer and mean squared error.
Development of Univariate LSTM Model 54:04
Develop a univariate LSTM for time series forecasting by forming lookback batches, testing with the same structure, and using a sliding window to generate successive future predictions.
Development of Univariate LSTM Model 625:36
Development of Multivariate LSTM Model 123:18
Development of Multivariate LSTM Model 218:33
Development of Multivariate LSTM Model 34:22
Explore testing a multivariate LSTM for time series by using a lookback window of multiple variables to predict the next target value and roll forward with predictions.
Development of Multivariate LSTM Model 417:20
Development of Multivariate LSTM Model 515:35

Requirements

General and Basic Python Skills

Description

"Time Series Analysis and Forecasting with Python" Course is an ultimate source for learning the concepts of Time Series and forecast into the future.

In this course, the most famous methods such as statistical methods (ARIMA and SARIMAX) and Deep Learning Method (LSTM) are explained in detail. Furthermore, several Real World projects are developed in a Python environment and have been explained line by line!

If you are a researcher, a student, a programmer, or a data science enthusiast that is seeking a course that shows you all about time series and prediction from A-Z, you are in a right place. Just check out what you will learn in this course below:

Basic libraries (NumPy, Pandas, Matplotlib)
How to use Pandas library to create DateTime index and how to set that as your Dataset index
What are statistical models?
How to forecast into future using the ARIMA model?
How to capture the seasonality using the SARIMAX model?
How to use endogenous variables and predict into future?
What is Deep Learning (Very Basic Concepts)
All about Artificial and Recurrent Neural Network!
How the LSTM method Works!
How to develop an LSTM model with a single variate?
How to develop an LSTM model using multiple variables (Multivariate)

As I mentioned above, in this course we tried to explain how you can develop an LSTM model when you have several predictors (variables) for the first time and you can use that for several applications and use the source code for your project as well!

This course is for Everyone! yes everyone! that wants t to learn time-series and forecasting into the future using statistics and artificial intelligence with any kind of background! Even if you are not a programmer, I show you how to code and develop your model line by line!

If you want to master the basics of Machine Learning in Python as well, you can check my other courses!

Who this course is for:

Data Science Enthusiast
Beginner Programmers
Python Developers
Recheachers who like to forecast into future
Data Analysts
Anyone who is interested in Time Series and Future Forecasting

Time Series Analysis and Forecasting with Python

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 24min

Useful Packages17 lectures • 3hr 47min

Pandas for Time Series Analysis4 lectures • 32min

Statistical Models for Time Series Forecasting8 lectures • 1hr 51min

Deep Learning for Time Series Forecasting17 lectures • 3hr 44min

Bonus1 lecture • 1min

Requirements

Description

Who this course is for: