Time Series Forecasting in R: A Down-to-Earth Approach
Time Series Forecasting in R: A Down-to-Earth Approach, available at $74.99, has an average rating of 4.5, with 81 lectures, based on 28 reviews, and has 296 subscribers.
You will learn about Know the time series forecasting steps Know the essential time series components Know the most important forecasting accuracy metrics Use the moving averages and the simple exponential smoothing techniques Use the advanced exponential smoothing techniques: Holt and Holt-Winters Use extended exponential smoothing models: TBATS and STLM Build regression models with trend only Build regression models with trend and seasonality Understand important concepts like autocorrelation, stationarity and integration Use the augmented Dickey-Fuller test for stationarity Build autoregressive integrated moving average models (ARIMA) Build neural networks for time series forecasting This course is ideal for individuals who are Students in any field that requires quantitative forecasts or Data analysts or Wanna be data analysts or Doctoral students or Any person who wants to develop their skills in time series analysis and forecasting It is particularly useful for Students in any field that requires quantitative forecasts or Data analysts or Wanna be data analysts or Doctoral students or Any person who wants to develop their skills in time series analysis and forecasting.
Enroll now: Time Series Forecasting in R: A Down-to-Earth Approach
Summary
Title: Time Series Forecasting in R: A Down-to-Earth Approach
Price: $74.99
Average Rating: 4.5
Number of Lectures: 81
Number of Published Lectures: 81
Number of Curriculum Items: 81
Number of Published Curriculum Objects: 81
Original Price: $89.99
Quality Status: approved
Status: Live
What You Will Learn
- Know the time series forecasting steps
- Know the essential time series components
- Know the most important forecasting accuracy metrics
- Use the moving averages and the simple exponential smoothing techniques
- Use the advanced exponential smoothing techniques: Holt and Holt-Winters
- Use extended exponential smoothing models: TBATS and STLM
- Build regression models with trend only
- Build regression models with trend and seasonality
- Understand important concepts like autocorrelation, stationarity and integration
- Use the augmented Dickey-Fuller test for stationarity
- Build autoregressive integrated moving average models (ARIMA)
- Build neural networks for time series forecasting
Who Should Attend
- Students in any field that requires quantitative forecasts
- Data analysts
- Wanna be data analysts
- Doctoral students
- Any person who wants to develop their skills in time series analysis and forecasting
Target Audiences
- Students in any field that requires quantitative forecasts
- Data analysts
- Wanna be data analysts
- Doctoral students
- Any person who wants to develop their skills in time series analysis and forecasting
Become the Best Time Series Expert in Your Organisation!
The goal of this course is to convert you into a highly-skilled time series forecaster. You will learn the most effective forecasting techniques that analysts use every day to make accurate predictions about the future. This will make you invaluable for your organisation and help you speed up your career like a flash. A time series analyst makes about $70,000 a year on average, but the top performers can make as much as $130,000 (according to SimplyHired).
This course will be a revolution for you, even if you don’t know anything about time series forecasting at this point. After completing it you will know how to…
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investigate historical data,
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detect trends and patterns
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choose the most appropriate forecasting methods
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assess forecasting accuracy
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reduce forecasting error
In a word, time series forecasting is a critical data science skill. If you want to be a full-blown data analyst you have to master time series.
Without further delay, let’s see what you are going to learn in this course.
In the first two sections (not counting the introduction) we build the foundations. The second section presents all the steps we must take to perform time series forecasting in practice, while in the third section you will become familiar with the essential time series notions. You will learn about trend and seasonality, time series decomposition, visualising trends, spotting seasonal patterns etc.
The fourth section is about evaluating forecasting performance. We will review the most used accuracy metrics for time series forecasting and explain them in detail. (We are going to use them extensively throughout the course.)
In the fifth section you will find a brief overview of the forecasting techniques approached in the course. The following sections examine these techniques in great detail and offer practical applications for each, using the R program.
The forecasting methods studied in this course are:
1. Moving averages (section 6). We don’t have to discard the simple forecasting methods, because sometimes they are more effective than the complex ones. This is why we start by looking into the moving averages, both simple and weighted.
2. Simple exponential smoothing (section 7), an extension of the moving averages method. In this section we introduce a very important R function for time series forecasting: ets. More details in the course.
3. Advanced exponential smoothing (section 8). Here we delve into really good stuff: we learn to forecast complicated series that present both trend and seasonal patterns. You will become familiar with two powerful models, Holt and Holt-Winters.
4. Extended exponential smoothing methods (section 9). In this chapter we will implement state-of-the-art models for series with double seasonality: TBATS and STLM.
5. Regression models (section 10). These models can be used for series with both trend and seasonality. They are easy to understand and apply.
6. Autoregressive – or ARIMA – models (sections 11 and 12). These models represent a must have tool for any time series forecaster. They can be extremely effective in many situations, since they can make predictions with a remarkable level of accuracy. In these section you’ll learn all-important concepts like autocorrelation, stationarity, integration, autoregressive processes and moving average processes. Afterwards you’ll learn how to identify an ARIMA model using the autocorrelation charts, how to build these models in R and how to use them for forecasting purposes.
7. Neural networks (section 13). Here we will deal with a special function that creates neural network models for time series forecasting.
Every technique is presented in video, both the syntax and the output being thoroughly explained. At the end of the course, a good number of practical exercises are proposed. This exercises will help you practice and improve your time series forecasting skills.
Join this course today and get hold of a mission critical ability – time series forecasting!
Course Curriculum
Chapter 1: Introduction
Lecture 1: What you'll learn in this course
Chapter 2: Forecasting Basics
Lecture 1: What Is Forecasting?
Lecture 2: Forecasting Steps
Lecture 3: Notations
Chapter 3: Time Series Data
Lecture 1: Time Series Components
Lecture 2: Zooming into Time Series
Lecture 3: Adding Trend Lines to Time Series
Lecture 4: Removing Seasonality
Chapter 4: Assessing Forecasting Performance
Lecture 1: Splitting Data
Lecture 2: Forecasting Accuracy Metrics
Lecture 3: Error Analysis
Chapter 5: Forecasting Methods: A Brief Summary
Lecture 1: Two Big Categories
Lecture 2: Data Driven Methods
Lecture 3: Model Based Methods
Chapter 6: Moving Averages
Lecture 1: What Are Moving Averages?
Lecture 2: Centered Moving Averages
Lecture 3: Trailing Moving Averages
Lecture 4: Weighted Moving Averages
Lecture 5: Computing and Plotting the Centered Moving Averages (First Example)
Lecture 6: Computing and Plotting the Centered Moving Averages (Second Example)
Lecture 7: Forecasting Weekly Sales With the Trailing Moving Average
Lecture 8: Assessing Weekly Sales Forecasting Accuracy
Lecture 9: Forecasting Weekly Sales With the Weighted Moving Averages
Chapter 7: Simple Exponential Smoothing
Lecture 1: What Is Simple Exponential Smoothing?
Lecture 2: Model Implementation
Lecture 3: Forecasting Weekly Sales With the Simple Exponential Smoothing
Lecture 4: Finding A Better Value For the Smoothing Parameter
Lecture 5: Forecasting Weekly Sales With A Confidence Interval
Chapter 8: Advanced Exponential Smoothing
Lecture 1: What Is Advanced Exponential Smoothing?
Lecture 2: The Holt Method With Additive Trend
Lecture 3: The Holt Method With Multiplicative Trend
Lecture 4: Forecasting Monthly Shampoo Sales With the Holt Model (Additive Trend)
Lecture 5: Finding A Better Value For the Smoothing Parameter (Alpha)
Lecture 6: Forecasting Future Shampoo Sales
Lecture 7: Forecasting Shampoo Sales With A Confidence Interval
Lecture 8: Forecasting Monthly Shampoo Sales With the Holt Model (Multiplicative Trend)
Lecture 9: Forecasting Future Shampoo Sales (Multiplicative Trend)
Lecture 10: Forecasting Daily MS Stock Prices With the Holt Model (Multiplicative Trend)
Lecture 11: Forecasting Future MS Stock Prices
Lecture 12: The Holt-Winters Method
Lecture 13: Forecasting Monthly Alcohol Sales With the Holt-Winters Model
Lecture 14: Forecasting Future Alcohol Sales
Lecture 15: Build An Automated Holt-Winters Model
Chapter 9: Extended Exponential Smoothing
Lecture 1: What Is Extended Exponential Smoothing?
Lecture 2: Forecasting Daily Bike Rentals With the TBATS Model
Lecture 3: Forecasting Future Number of Rentals With TBATS
Lecture 4: Forecasting Daily Bike Rentals With the STLM Model
Lecture 5: Forecasting Future Number of Rentals With STLM
Chapter 10: Regression Models
Lecture 1: Regression Models – A Definition
Lecture 2: Regression Models With Trend Only
Lecture 3: Forecasting Monthly Shampoo Sales With Linear Regression
Lecture 4: Forecasting Monthly Shampoo Sales With Quadratic Regression
Lecture 5: Forecasting Monthly Shampoo Sales With Cubic Regression
Lecture 6: Forecasting Monthly Shampoo Sales With Exponential Regression
Lecture 7: Forecasting Daily MS Stock Prices With Quadratic Regression
Lecture 8: Regression Models With Trend and Seasonality
Lecture 9: Forecasting Monthly Alcohol Sales Sales (1)
Lecture 10: Forecasting Monthly Alcohol Sales (2)
Lecture 11: Forecasting Monthly Electricity Production
Chapter 11: Autoregressive Models – Fundamentals
Lecture 1: Introduction to Autoregressive Models
Lecture 2: The Concept of Autocorrelation
Lecture 3: The Concept of Stationarity
Lecture 4: The Concept of Differencing
Lecture 5: The AR Process
Lecture 6: The MA Process
Lecture 7: The ARMA Process
Lecture 8: The ARIMA Process
Lecture 9: Identifying the ARIMA Model
Chapter 12: Autoregressive Models – Practice
Lecture 1: Forecasting Weekly Product Sales With ARIMA
Lecture 2: Forecasting Weekly Product Sales With ARIMA – Trying A Simple Model
Lecture 3: Forecasting Monthly Alcohol Sales With ARIMA
Lecture 4: Forecasting Monthly Electricity Production With ARIMA
Lecture 5: Forecasting Daily MS Stock Prices With ARIMA
Chapter 13: Neural Networks
Lecture 1: Neural Network Basics
Lecture 2: Model Preparation
Lecture 3: The nnetar Function
Lecture 4: Forecasting Monthly Shampoo Sales With Neural Networks
Lecture 5: Forecasting Monthly Alcohol Sales With Neural Networks
Lecture 6: Forecasting Daily MS Stock Prices With Neural Networks
Chapter 14: Exercises
Lecture 1: Practical Exercises
Chapter 15: Documents and files
Lecture 1: Download Links
Instructors
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Bogdan Anastasiei
University Teacher and Consultant
Rating Distribution
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- 2 stars: 1 votes
- 3 stars: 0 votes
- 4 stars: 12 votes
- 5 stars: 15 votes
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