# Learn Data Science for Business ## Become a Data Scientist in our online courses Earn 6 figures or more in 6 months or less by learning R, Shiny, Machine Learning, Time Series, Web Apps, AWS, Cloud, and more! 5-10 Hours Per Week.  80/20 Skills.  End-To-End Business Projects. [Start Your Courses Now! 👉](https://university.business-science.io/p/5-course-bundle-machine-learning-web-apps-time-series?el=website) Join over 100,000+ Data Scientists # The Top 5 Time Series Analysis Concepts (that helped me the most in my career) _Written by Matt Dancho_ * * * Hey guys, welcome back to my [R-tips newsletter](https://learn.business-science.io/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) * * * # 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.** # 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](https://learn.business-science.io/r-tips-newsletter?el=website). **All code is available 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)](https://learn.business-science.io/r-tips-newsletter?el=website) ## 2\. 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)](https://learn.business-science.io/r-tips-newsletter?el=website) ## 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)](https://learn.business-science.io/r-tips-newsletter?el=website) ## 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)](https://learn.business-science.io/r-tips-newsletter?el=website) ## 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)](https://learn.business-science.io/r-tips-newsletter?el=website) # 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](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting?el=website). **However, there is A LOT more to becoming an expert in time series for your company.** ## Take the High-Performance Forecasting Course > Become the forecasting expert for your organization [_High-Performance Time Series_\ _Course_](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting?el=website) ### 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**](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting?el=website). 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.