Forecasting large collections of time series

With the recent launch of Amazon Forecast, I can no longer procrastinate writing about forecasting “at scale”! Quantitative forecasting of time series has been used (and taught) for decades, with applications in many areas of business such as demand forecasting, sales forecasting, and financial forecasting. The types of methods taught in forecasting courses tends to be discipline-specific: Statisticians love ARIMA (auto regressive integrated moving average) models, with multivariate versions such as Vector ARIMA, as well as state space models and non-parametric methods such as STL decompositions. Econometricians and finance academics go one step further into ARIMA variations such as ARFIMA (f=fractional), … Continue reading Forecasting large collections of time series

A Tale of Two (Business Analytics) Courses

I have been teaching two business analytics elective MBA-level courses at ISB. One is called “Business Analytics Using Data Mining” (BADM) and the other, “Forecasting Analytics” (FCAS). Although we share the syllabi for both courses, I often receive the following question, in this variant or the other: What is the difference between the two courses? The short answer is: BADM is focused on analyzing cross-sectional data, while FCAS is focused on time series data. This answer clarifies the issue to data miners and statisticians, but sometimes leaves aspiring data analytics students perplexed. So let me elaborate. What is the difference … Continue reading A Tale of Two (Business Analytics) Courses

Launched new book website for Practical Forecasting book

Last week I launched a new website for my textbook Practical Time Series Forecasting. The website offers resources such as the datasets used in the book, a block with news that pushes posts to the book Facebook page, information about the book and author, for instructors an online form for requesting an evaluation copy and another for requesting access to solutions, etc. I am already anticipating my colleagues’ question “what platform did you use?”. Well, I did not hire a web designer, nor did I spend three months putting the website together using HTML. Instead, I used Google Sites. This … Continue reading Launched new book website for Practical Forecasting book

Forecasting + Analytics = ?

Quantitative forecasting is an age-old discipline, highly useful across different functions of an organization: from  forecasting sales and workforce demand to economic forecasting and inventory planning. Business schools have offered courses with titles such as “Time Series Forecasting”, “Forecasting Time Series Data“, “Business Forecasting“,  more specialized courses such as “Demand Planning and Sales Forecasting” or even graduate programs with title “Business and Economic Forecasting“. Simple “Forecasting” is also popular. Such courses are offered at the undergraduate, graduate and even executive education. All these might convey the importance and usefulness of forecasting, but they are far from conveying the coolness of forecasting. … Continue reading Forecasting + Analytics = ?

“Predict” or “Forecast”?

What is the difference between “prediction” and “forecasting”? I heard this being asked quite a few times lately. The Predictive Analytics World conference website has a Predictive Analytics Guide page with the following Q&A: How is predictive analytics different from forecasting? Predictive analytics is something else entirely, going beyond standard forecasting by producing a predictive score for each customer or other organizational element. In contrast, forecasting provides overall aggregate estimates, such as the total number of purchases next quarter. For example, forecasting might estimate the total number of ice cream cones to be purchased in a certain region, while predictive analytics tells you which individual … Continue reading “Predict” or “Forecast”?

Visualizing time series: suppressing one pattern to enhance another pattern

Visualizing a time series is an essential step in exploring its behavior. Statisticians think of a time series as a combination of four components: trend, seasonality, level and noise. All real-world series contain a level and noise, but not necessarily a trend and/or seasonality. It is important to determine whether trend and/or seasonality exist in a series in order to choose appropriate models and methods for descriptive or forecasting purposes. Hence, looking at a time plot,  typical questions include: is there a trend? if so, what type of function can approximate it? (linear, exponential, etc.) is the trend fixed throughout the period … Continue reading Visualizing time series: suppressing one pattern to enhance another pattern

Lots of real time series data!

I love data-mining or statistics competitions – they always provide great real data! However, the big difference between a gold mine and “just some data” is whether the data description and their context is complete. This reflects, in my opinion, the difference between “data mining for the purpose of data mining” vs. “data mining for business analytics” (or any other field of interest, such as engineering or biology). Last year, the BICUP2006 posted an interesting dataset on bus ridership in Santiego de Chile. Although there was a reasonable description of the data (number of passengers at a bus stations at … Continue reading Lots of real time series data!