smooth package has reached version 3.2.0 and is now on CRAN. While the version change from 3.1.7 to 3.2.0 looks small, this has introduced several substantial changes and represents a first step in moving to the new C++ code in the core of the functions. In this short post, I will outline the main new […]
R
Smooth forecasting with the smooth package in R
Authors: Ivan Svetunkov Abstract: There are many forecasting related packages in R with varied popularity, the most famous of all being forecast, which implements several important forecasting approaches, such as ARIMA, ETS, TBATS and others. However, the main issue with the existing functionality is the lack of flexibility for research purposes, when it comes to […]
Complex Exponential Smoothing
Authors: Ivan Svetunkov, Nikolaos Kourentzes, Keith Ord. Journal: Naval Research Logistics Abstract: Exponential smoothing has been one of the most popular forecasting methods used to support various decisions in organisations, in activities such as inventory management, scheduling, revenue management and other areas. Although its relative simplicity and transparency have made it very attractive for research […]
The first draft of “Forecasting and Analytics with ADAM”
After working on this for more than a year, I have finally prepared the first draft of my online monograph “Forecasting and Analytics with ADAM“. This is a monograph on the model that unites ETS, ARIMA and regression and introduces advanced features in univariate modelling, including: ETS in a new State Space form; ARIMA in […]
An Integrated Method for Estimation and Optimisation
My PhD student, Congzheng Liu (co-supervised with Adam Letchford) has written a paper, entitled “Newsvendor Problems: An Integrated Method for Estimation and Optimisation“. This paper has recently been published in EJOR. In this paper we build upon the existing Ban & Rudin (2019) approach for newsvendor problem, showing that in case of the linear model, […]
After the creation of ADAM: smooth v3.1.0
Since the previous post on “The Creation of ADAM“, I had difficulties finding time to code anything, but I still managed to fix some bugs, implement a couple of features and make changes, important enough to call the next version of package smooth “3.1.0”. Here is what’s new: A new algorithm for ARIMA order selection […]
The creation of ADAM – next step in statistical forecasting
Good news everyone! The future of statistical forecasting is finally here :). Have you ever struggled with ETS and needed explanatory variables? Have you ever needed to unite ARIMA and ETS? Have you ever needed to deal with all those zeroes in the data? What about the data with multiple seasonalities? All of this and […]
Accuracy of forecasting methods: Can you tell the difference?
Previously we discussed how to measure accuracy of point forecasts and performance of prediction intervals in different cases. Now we look into the question how to tell the difference between competing forecasting approaches. Let’s imagine the situation, when we have four forecasting methods applied to 100 time series with accuracy measured in terms of RMSSE: […]
How confident are you? Assessing the uncertainty in forecasting
Introduction Some people think that the main idea of forecasting is in predicting the future as accurately as possible. I have bad news for them. The main idea of forecasting is in decreasing the uncertainty. Think about it: any event that we want to predict has some systematic components \(\mu_t\), which could potentially be captured […]
Are you sure you’re precise? Measuring accuracy of point forecasts
Two years ago I have written a post “Naughty APEs and the quest for the holy grail“, where I have discussed why percentage-based error measures (such as MPE, MAPE, sMAPE) are not good for the task of forecasting performance evaluation. However, it seems to me that I did not explain the topic to the full […]