Master time series forecasting from statistical to state-of-the-art deep learning models in 100% Python code

What you'll learn

The basics of time series forecasting using baseline models

Apply statistical models like ARIMA, ETS, TBATS and more

Apply deep learning architectures for time series forecasting

Use state-of-the-art deep learning models like NHITS, TSMixer, iTransformer, TimeGPT, and more!

Requirements

Basic knowledge of Python

Description

Master Time Series Forecasting: From Fundamentals to Deep LearningUnlock the power of predictive analytics in this comprehensive 12-hour course designed specifically for aspiring data scientists. Whether you're looking to forecast market trends, optimize supply chains, or predict weather patterns, this course will equip you with the essential skills to tackle real-world forecasting challenges.What You'll LearnTransform from a beginner to a confident practitioner through our carefully structured curriculum. Starting with fundamental statistical models, you'll progress to implementing cutting-edge deep learning architectures. Along the way, you'll master:Classical forecasting methods (ARIMA, SARIMA, SARIMAX)Advanced techniques like exponential smoothing, TBATS, and the Theta modelDeep learning architectures for time seriesFacebook's Prophet frameworkState-of-the-art models for complex forecasting challengesSpecialized approaches for intermittent time seriesWhy This Course Stands Out14+ hands-on projects that reinforce your learning100% Python-based curriculum with complete code implementationsReal-world applications across finance, economics, retail, and supply chainProgressive learning path from basics to advanced conceptsExclusive content on state-of-the-art forecasting modelsPerfect For You If.You're new to time series forecasting but have basic Python programming skills. No prior forecasting experience needed – we'll guide you through every step, from understanding the fundamentals to implementing advanced predictive models.Course StructureThe curriculum flows naturally from foundational concepts to advanced applications:Core statistical methods and their practical implementationMultivariate forecasting techniques for complex datasetsDeep learning approaches built from the ground upModern frameworks and state-of-the-art architecturesSpecial topics in intermittent demand forecastingAbout Your InstructorLearn from an industry expert at the forefront of time series innovation. I am a contributor at Nixtla, a leader in open-source forecasting technology, and an active developer of NeuralForecast, the Python package renowned for its lightning-fast deep learning implementations. This isn't just theoretical knowledge – it's practical insight from someone who shapes the tools that industry leaders use today.By the end of this course, you'll have the skills and confidence to tackle diverse forecasting challenges across any industry. Join us to master one of the most valuable skills in data science, backed by extensive hands-on practice and real-world applications.Ready to predict the future? Enroll now and transform your data science journey.

Overview

Section 1: Introduction

Lecture 1 Welcome

Lecture 2 Defining time series

Lecture 3 Baseline models

Lecture 4 Code - Baseline models

Section 2: The random walk model

Lecture 5 Introducing the random walk

Lecture 6 Code - Simulate a random walk

Lecture 7 Stationarity and differencing

Lecture 8 Code - Stationarity and differencing

Lecture 9 Autocorrelation

Lecture 10 Code - Autocorrelation

Lecture 11 Forecasting a random walk

Lecture 12 Code - Forecasting a random walk

Section 3: Forecasting with the ARIMA model

Lecture 13 The moving average model

Lecture 14 Code - Forecasting with MA(q)

Lecture 15 The autoregressive model

Lecture 16 Code - Forecasting with AR(p)

Lecture 17 The ARMA model

Lecture 18 Designing a general modeling procedure

Lecture 19 Code - Forecasting with ARMA(p,q)

Lecture 20 The ARIMA model

Lecture 21 Code - Forecasting with ARIMA(p,d,q)

Lecture 22 Modeling seasonality

Lecture 23 Code - Forecasting with SARIMA

Lecture 24 Adding external variables to our model

Lecture 25 Code - Forecasting with SARIMAX

Section 4: Multivariate forecasting

Lecture 26 Multivariate forecasting

Lecture 27 Code - Forecasting with VAR

Lecture 28 Code - Forecasting with VARMA

Lecture 29 Code - Forecasting with VARMAX

Section 5: Exponential smoothing

Lecture 30 Simple exponential smoothing

Lecture 31 Code - Forecasting with simple exponential smoothing

Lecture 32 Double exponential smoothing

Lecture 33 Code - Forecasting with double exponential smoothing

Lecture 34 Triple exponential smoothing

Lecture 35 Code - Forecasting with triple exponential smoothing

Section 6: Forecasting multiple seasonal periods

Lecture 36 BATS and TBATS

Lecture 37 Code - Forecasting with BATS and TBATS

Section 7: Forecasting using decomposition

Lecture 38 The Theta model

Lecture 39 Code - Forecasting with the Theta model

Lecture 40 Code - Comparing Theta to SARIMA

Section 8: Deep learning for time series forecasting

Lecture 41 Introducing deep learning for time series forecasting

Lecture 42 Code - Preprocessing data for deep learning

Lecture 43 Linear models

Lecture 44 Code - Linear models

Lecture 45 Deep neural networks

Lecture 46 Code - Deep neural networks

Lecture 47 LSTM

Lecture 48 Code - LSTM

Lecture 49 Code - CNN

Lecture 50 CNN

Section 9: EXTRA - Prophet

Lecture 51 Understanding Prophet

Lecture 52 Code - Get started with Prophet

Lecture 53 Advanced features of Prophet

Lecture 54 Code - Advanced features of Prophet

Lecture 55 Hyperparameter tuning with Prophet

Lecture 56 Code - Hyperparameter tuning with Prophet

Lecture 57 Code - Forecasing with Prophet

Section 10: EXTRA - State-of-the-art forecasting

Lecture 58 N-BEATS

Lecture 59 Code - NBEATS

Lecture 60 NHITS

Lecture 61 Code - NHITS

Lecture 62 PatchTST

Lecture 63 Code - PatchTST

Lecture 64 TimesNet

Lecture 65 Code - TimesNet

Lecture 66 TiDE

Lecture 67 Code - TiDE

Lecture 68 TSMixer

Lecture 69 Code - TSMixer

Lecture 70 iTransformer

Lecture 71 Code - iTransformer

Lecture 72 SOFTS

Lecture 73 Code - SOFTS

Lecture 74 RMoK

Lecture 75 Code - RMoK

Section 11: EXTRA - Forecasting intermittent time series

Lecture 76 Introduction to intermittent time series forecasting

Lecture 77 Croston's method

Lecture 78 Code -Croston's method

Lecture 79 ADIDA and IMAPA

Lecture 80 Code - ADIDA and IMAPA

Lecture 81 TSB

Lecture 82 Code - TSB

Lecture 83 Error metrics for intermittent time series forecasting

Lecture 84 Code - Error metrics for intermittent time series forecasting

Lecture 85 Code - Forecast the monthly sales of car parts

Beginners eager to learn about time series forecasting,Practitioners looking to perfect their forecasting skills,Anyone serious about mastering time series forecasting using state-of-the-art models