Complete Time Series Forecasting Bootcamp In Python (2025)

Complete Time Series Forecasting Bootcamp In Python (2025)
Published 1/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

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

Requirements

Basic knowledge of Python

Description

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 25 Code – Forecasting with SARIMAX

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 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

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