Published 11/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.54 GB | Duration: 2h 28m
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
escribePredictExplainControlOne 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.
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