# Introduction to Time Series Analysis (with applications in R)

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Hey guys, welcome back to my R-tips newsletter. Time series analysis has been critical in my career. But it took me 3 years to get comfortable. In today’s R-Tip, I’ll share 3 years of experience in time series in 3 minutes. Let’s go!

### Table of Contents

Here’s what you’re learning today:

**What is Time Series Analysis?**I’ll explain what time series analysis is and why it was important to me to learn it.**The 5 Concepts that Helped Me the Most in My Career**. I’ll share the 5 concepts that helped me the most in my career.**How to Make the 5 Top Time Series Visualizations in 5 lines of R code**. I’ll show you how to make the 5 top time series visualizations in 5 lines of R code.

Time Series Analysis (Top 5 Visualizations)

# SPECIAL ANNOUNCEMENT: ChatGPT for Data Scientists Workshop on January 17th

Inside the workshop I’ll share how I built a Machine Learning Powered Production Shiny App with `ChatGPT`

(extends this data analysis to an *insane* production app):

**What:** ChatGPT for Data Scientists

**When:** Wednesday January 17th, 2pm EST

**How It Will Help You:** Whether you are new to data science or are an expert, ChatGPT is changing the game. There’s a ton of hype. But how can ChatGPT actually help you become a better data scientist and help you stand out in your career? I’ll show you inside my free chatgpt for data scientists workshop.

**Price:** Does **Free** sound good?

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# R-Tips Weekly

This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks. Pretty cool, right?

Here are the links to get set up. 👇

# What is Time Series Analysis?

Time series analysis is a statistical technique that deals with time-ordered data points. It’s commonly used to analyze and interpret trends, patterns, and relationships within data that is recorded over time (e.g. with timestamps).

## Uses in Business

Understanding and applying time series analysis concepts is critical for **forecasting, detecting anomalies, and drawing insights on data that varies over time.**

**Time series data is everywhere.** Anything with a timestamp is a time series. Product sales, website traffic, stock prices, and weather data are all examples of time series data. It is used in many industries including finance, retail, marketing, and manufacturing.

**Time Series Analysis is important because it allows us to understand the past and predict the future.** Time series analysis is used to understand the past and predict the future. It is used in many industries including finance, retail, marketing, and manufacturing.

# The 5 Concepts that Helped Me the Most in My Career (and how to do them in `R`

)

The 5 Concepts that helped me the most

## R Code

**Get The Code:** You can follow along with the R code in the R-Tips Newsletter. **All code is avaliable in R-Tip 075.**

## 1. Visualizing Time Series Data

Visualizing time series is the start of all of my time series analysis. This is the first step in understanding the data.

`R`

code to make this plot:

The main functions come from `timetk`

. Full disclosure- I’m the author of `timetk`

. I created `timetk`

to make time series analysis easier.

Get the Code (In the R-Tip 075 Folder)

## Time Series is Noisy (Finding the Signal)

Often, time series data is noisy. We can use smoothing to find the signal. LOESS smoothing is a technique that uses local regression to smooth out the noise.

`R`

code to make Visualization 2:

It’s the same function, but now we turn `.smooth = TRUE`

. You can adjust the value of the smoother span to get different results.

Get the Code (In the R-Tip 075 Folder)

## 3. Autocorrelation and Partial Autocorrelation

**Autocorrelation:** This refers to the correlation of a time series with its own past and future values. It measures the relationship (correlation) between a variable’s current value and its past values.

**Partial Autocorrelation:** Autocorrelation has a problem. Some of the correlation is confounded by earlier lags. Enter Partial Autocorrelation. This removes the correlation effect of earlier lags. We can see that Lag 1 and 6 are the most important for this time series.

`R`

Code to make this plot:

Get the Code (In the R-Tip 075 Folder)

## 4. Seasonal Decomposition

Seasonal decomposition decomposes a time series into three components: **trend, seasonal, and residual (irregular)**. STL stands for Seasonal-Trend-Loess.

**It uses a “LOESS” smoother** to remove seasonal and trend effects. STL is flexible and can handle any type of seasonality, not just fixed seasonal effects.

**The residuals** can be analyzed for outliers since they have been de-trended and de-seasonalized.

`R`

Code to make this plot:

Get the Code (In the R-Tip 075 Folder)

## 5. Calendar Effects

Calendar effects refer to variations in a time series that can be attributed to the calendar itself. This can include effects due to day of the week, month of the year, or holidays tied to the calendar.

`R`

Code to make this plot:

Get the Code (In the R-Tip 075 Folder)

# Conclusions:

You’ve learned the 5 concepts that helped me the most in my career. And the best part is that you can do all of this in 5 lines of R code.

Here’s another little secret, I teach these concepts plus others in just Module 1 of 18 in my High-Performance Time Series Course.

**However, there is A LOT more to becoming an expert in time series for your company.**

If you want to become a Time Series Expert for your company, then please read on…

## Take the High-Performance Forecasting Course

Become the forecasting expert for your organization

*High-Performance Time Series
Course*

### Time Series is Changing

Time series is changing. **Businesses now need 10,000+ time series
forecasts every day.** This is what I call a *High-Performance Time
Series Forecasting System (HPTSF)* – Accurate, Robust, and Scalable
Forecasting.

**High-Performance Forecasting Systems will save companies by improving
accuracy and scalability.** Imagine what will happen to your career if
you can provide your organization a “High-Performance Time Series
Forecasting System” (HPTSF System).

### How to Learn High-Performance Time Series Forecasting

I teach how to build a HPTFS System in my **High-Performance Time
Series Forecasting
Course**.
You will learn:

**Time Series Machine Learning**(cutting-edge) with`Modeltime`

– 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)**Deep Learning**with`GluonTS`

(Competition Winners)**Time Series Preprocessing**, Noise Reduction, & Anomaly Detection**Feature engineering**using lagged variables & external regressors**Hyperparameter Tuning****Time series cross-validation****Ensembling**Multiple Machine Learning & Univariate Modeling Techniques (Competition Winner)**Scalable Forecasting**– Forecast 1000+ time series in parallel- and more.

Become the Time Series Expert for your organization.

Take the High-Performance Time Series Forecasting Course

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