Time Series Analysis For Beginner From Scratch

Fundamentals of time series analysis

What you'll learn

Learn the basic statistical concepts and techniques used in time series analysis.

Learn some basic statements to do a time series analysis using Python.

Learn some basic statements to do a time series analysis using R.

Explain in your own terms, how to perform a time series analysis.

Identify the type of model to apply in a time series.

Requirements

Skill basic knowledge about Statistics, Python, R.

Programming experience is desirable, but not needed. We will see some basic structure query language syntax for data wrangling.

Description

There are several reasons why it is desirable to study a time series.In general, we can say that, the study of a time series has as main objectives:DescribePredictExplainControlOne of the most important reasons for studying time series is for the purpose of making forecasts about the analyzed time series.The reason that forecasting is so important is that prediction of future events is critical input into many types of planning and decision-making processes, with application to areas such Marketing, Finance Risk Management, Economics, Industrial Process Control, Demography, and so forth.Despite the wide range of problem situations that require forecasts, there are only two broad types of forecasting techniques. These are Qualitative methods and Quantitative methods.Qualitative forecasting techniques are often subjective in nature and require judgment on the part of experts.Quantitative forecasting techniques make formal use of historical data and a forecasting model. The model formally summarizes patterns in the data and expresses a statistical relationship between previous (Tn-1), and current values (Tn), of the variable.In other words, the forecasting model is used to extrapolate past and current behavior into the future. That's what we'll be learning in this course.Regardless of your objective, this course is oriented to provide you with the basic foundations and knowledge, as well as a practical application, in the study of time series.Students will find valuable resources, in addition to the video lessons, it has a large number of laboratories, which will allow you to apply in a practical way the concepts described in each lecture.The labs are written in two of the most important languages in data science. These are python and r.

Overview

Section 1: Environment Preparation

Lecture 1 Install Anaconda Individual Edition

Lecture 2 Install RStudio Free Edition

Section 2: Time Series - Concepts

Lecture 3 Basic Concepts

Lecture 4 Time Series Components

Lecture 5 Time Series Decomposition Analysis

Section 3: Data Wrangling

Lecture 6 Loading Data

Lecture 7 Summary Data Part 1

Lecture 8 Summary Data - Part 2

Section 4: Differencing

Lecture 9 Differencing and Random Walk

Lecture 10 Order Differencing

Section 5: Time Series Models bases

Lecture 11 Autoregressive Model

Lecture 12 Moving Average Model

Lecture 13 BackShift Operator

Lecture 14 Difference Operator

Lecture 15 Auto Correlation Function

Lecture 16 Partial Autocorrelation Function

Section 6: Time Series Models

Lecture 17 ARMA Model

Lecture 18 ARIMA Model

Lecture 19 Dickey Fuller Test

Lecture 20 Ljung-Box Q-statistics

Lecture 21 Model Basic Steps

Lecture 22 SARIMA Model

Section 7: Forecasting

Lecture 23 Forecast

Lecture 24 Simple Exponential Smoothing - Part 1

Lecture 25 Simple Exponential Smoothing - Part 2

Lecture 26 Holt's Exponential Smoothing

Students who wish to acquire or improve their skills in data analysis through time series techniques.,Python developers who want to improve their skills using time series techniques.,Data Analysts.,Beginning python and r developers interested in data science.,Professionals in areas such as marketing, finance, retail, budget, production stock, and so forth.