Practical Python For Time Series Analysis & Modelling

A course to learn how to handle time series through practical cases, especially in the energy sector.

What you'll learn

Handling time series al dente

Visually analyse a series with a variety of charts

Model the future with statistical and Deep Learning algorithms

Decompose and differentiate the elements that make up the time series

Requirements

We'll explain everything step by step, starting from blank notebooks. Therefore, any beginner can take the course. However, if you're already familiar with Python or data analysis, you'll progress more comfortably.

Description

### Course DescriptionWould you like to master the analysis and modeling of time series using Python? This course is perfect for you. Through practical cases from the energy sector, you will learn how to handle temporal data, identify patterns, and make accurate predictions.In this course, you will acquire essential skills to:- **Preprocess and visualize temporal data**: Learn how to clean and prepare your data for detailed analysis, and create visualizations that make it easier to identify trends and patterns.- **Decompose time series**: Understand how to separate trend, seasonality, and noise components in both additive and multiplicative models.- **Ensure stationarity and manage seasonality**: Discover key techniques to transform your data and make modeling easier.- **Apply statistical and deep learning models**: Develop robust predictive models using traditional statistical methods and advanced neural network techniques.- **Evaluate and improve your models**: Use error metrics and data scaling techniques to enhance the accuracy of your predictions.### Who is this course for?- **Data analysts and scientists**: Who want to deepen their understanding of time series analysis and apply this knowledge in real-world projects.- **Energy sector professionals**: Who need to optimize the management and prediction of resources through the use of historical data and predictive models.- **Students and data analysis enthusiasts**: Who wish to specialize in one of the most in-demand areas and learn practical techniques applicable across multiple industries.### MethodologyThe course is designed to be highly practical. Through real-world examples and interactive exercises, you'll be able to immediately apply what you learn. Each module is structured to provide both fundamental theory and practical application, ensuring a comprehensive and applicable understanding of time series concepts.

Overview

Section 1: Get prepared

Lecture 1 Download materials

Lecture 2 Work with programs locally

Section 2: Manipulation, Preprocessing, and Visualization

Lecture 3 Introduction

Lecture 4 Load and preprocess temporal columns

Lecture 5 Pivot tables

Lecture 6 Automatic temporal resampling

Lecture 7 Resampling to group noise

Lecture 8 Correlation and interactive visualization with Plotly

Lecture 9 Melting pivot tables

Lecture 10 Correlation matrix

Lecture 11 Rankings with pivot tables

Lecture 12 Exam: Energy prices by markets

Section 3: Time Series Decomposition

Lecture 13 "Time series decomposition"

Lecture 14 Component visualization

Lecture 15 Additive vs multiplicative model

Lecture 16 Exam: Daily vs monthly solar generation

Section 4: Seasonality and stationarity

Lecture 17 Differencing a time series

Lecture 18 Exam: Photovoltaic solar generation

Section 5: Forecasting I

Lecture 19 Baseline Models

Lecture 20 Statistical Models

Lecture 21 ARIMA Models

Lecture 22 ACF & PACF

Lecture 23 SARIMA

Lecture 24 Grid search to select the best parameters

Lecture 25 Exam: ARIMA

Section 6: Model Comparison and Evaluation

Lecture 26 Error: calculation and interpretation

Lecture 27 Different formulas to calculate error

Lecture 28 Train test split

Lecture 29 Model comparison

Section 7: Forecasting II: LSTM Neural Networks

Lecture 30 Introduction

Lecture 31 Create Python environment for TensorFlow

Lecture 32 Preprocess time series

Lecture 33 Input dimension

Lecture 34 Early stopping to save computation

Lecture 35 Evaluation: actual vs predicted data

Lecture 36 Interpretation: RMSE and MAE

Beginners who want to learn while solving exercises based on real-world practical cases.,Professionals who work with time series in Excel and want to transition to Python to better automate their processes.,Energy sector professionals who need to interpret statistics and mathematical models for time series.