Loading data into xts object

1. Loading data into xts object

Hello! I'm Aric LaBarr and I will be your instructor for this course on forecasting product demand. Before we can do anything fancy with modeling, we need to understand how to bring our data into R and make it ready for time series analysis. To do this we are going to use xts objects!

2. xts objects

3. Loading Data Into xts Object

To make the data easier to forecast, we will be making an xts object out of the data. An xts object stands for extensible time series. It essentially builds upon zoo objects which are commonly used for time structured data in R. The easiest way I like to think about xts objects are essentially a data matrix that is indexed across time. This indexing makes exploration and manipulation of the data extremely easy. If you are interested in learning more granular details about xts objects,

4. DataCamp courses about xts

I highly recommend taking these DataCamp courses.

5. Loading Data Example

Remember that an xts object contains two pieces - the date index and the data matrix. The data has already been loaded into a matrix for you, but we need to create a date index. Luckily it is rather easy to do just that with the `seq` function in R. Just specify a starting point, which is typically easier to do with an as dot Date function, a length of the vector of dates, and what unit of time you would like the vector to be in. Here we want 176 weeks starting on January 19, 2014. Creating an xts object from there is extremely easy using the `xts` function! Just specify your data matrix - here called bev - and your date index with the `order dot by` option. Now each of your observations is indexed by a date as you can see here for our mountain high end product called 'M dot hi' in our data set. One nuance you can see here about `xts` objects are that you can call columns by name inside of `xts` objects. For example, your `xts` object called `bev_xts` with a column named `M dot hi` you could call that column specifically as you see here in the slides.

6. Let's practice!

Now you try manipulating data with the xts function!.

This exercise is part of the course

Forecasting Product Demand in R

IntermediateSkill Level

4.8+

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When it comes to forecasting, time series modeling is a great place to start! You need to forecast out the future values of sales demand and a good baseline approach would be ARIMA models. In this chapter you'll learn how to quickly implement ARIMA models and get good initial forecasts for future product demand.

Exercise 1: Loading data into xts object

Current Exercise

Exercise 2: Importing data Exercise 3: Plotting / visualizing data Exercise 4: ARIMA Time Series 101 Exercise 5: auto.arima() function Exercise 6: Interpret auto.arima Exercise 7: Forecasting and Evaluating Exercise 8: Forecast function Exercise 9: Calculating MAPE and MAE Exercise 10: Visualizing Forecast Exercise 11: Confidence Intervals for Forecast

Economic theory has a lot to say about predicting values of demand. Obviously, external factors like price, seasonality, and timing of promotions will drive some aspects of product demand. In this chapter you'll learn about the basics around price elasticity models and how to incorporate seasonality and promotion timing factors into our product demand forecasts.

Exercise 1: Price elasticity Exercise 2: Calculating price elasticity Exercise 3: Interpret results from elasticity Exercise 4: Seasonal / holiday / promotional effects Exercise 5: Visualize holiday / promotion effects Exercise 6: Create holiday / promotional effect variables Exercise 7: Regression for holiday / promotional effects Exercise 8: Significant holiday or promotional effects?Exercise 9: Forecasting with regression Exercise 10: Create future predictor variables Exercise 11: Forecast future values of demand Exercise 12: Visualizing forecasts of regression

Time series models and pricing regressions don't have to be thought of as separate approaches to product demand forecasting. They can be combined! In this chapter you'll learn about two ways of "combining" the information gained in both modeling approaches - transfer functions and forecast ensembling.

Exercise 1: Residuals from regression model Exercise 2: Calculating residuals from regression Exercise 3: Visualizing residuals Exercise 4: Forecasting residuals Exercise 5: auto.arima function Exercise 6: Forecasting residuals with time series Exercise 7: Visualizing residual forecasts Exercise 8: Transfer functions & ensembling Exercise 9: Combining residuals from regression & time series Exercise 10: Visualizing transfer function forecast Exercise 11: Calculating transfer function MAPE and MAE Exercise 12: ARIMA Forecasting Exercise 13: Ensembling of Forecasts

Everything up until this point deals with making individual models for forecasting product demand. However, we haven't taken advantage of the fact that all of these products form a product hierarchy of sales. Products make up regions and regions make up states. How can we ensure that our forecasts reconcile correctly up and down the hierarchy? In this chapter you'll learn about hierarchical forecasting and how to use it to your advantage in forecasting product demand.

Exercise 1: Bottom-Up Hierarchical Forecasting Exercise 2: Build time series forecast for new product Exercise 3: Build regression forecast for new product Exercise 4: Ensemble forecast for new product Exercise 5: Calculate bottom-up forecast for whole region Exercise 6: Top-Down Hierarchical Forecasting Exercise 7: Build time series forecast at regional level Exercise 8: Using average historical proportions Exercise 9: Using proportion of historical averages Exercise 10: Middle-Out Hierarchical Forecasting Exercise 11: Build time series forecast for new region Exercise 12: Top-down forecast for new region Exercise 13: Bottom-up forecast for whole state Exercise 14: Congratulations!