Galit Shmueli - Collected Quotes

"Extreme values are values that are unusually large or small compared to other values in the series. Extreme va- lue can affect different forecasting methods to various degrees. The decision whether to remove an extreme value or not must rely on information beyond the data. Is the extreme value the result of a data entry error? Was it due to an unusual event (such as an earthquake) that is unlikely to occur again in the forecast horizon? If there is no grounded justification to remove or replace the extreme value, then the best practice is to generate two sets of forecasts: those based on the series with the extreme values and those based on the series excluding the extreme values." (Galit Shmueli, "Practical Time Series Forecasting: A Hands-On Guide", 2011)

"For the purpose of choosing adequate forecasting methods, it is useful to dissect a time series into a systematic part and a non-systematic part. The systematic part is typically divided into three components: level , trend , and seasonality. The non-systematic part is called noise. The systematic components are assumed to be unobservable, as they characterize the underlying series, which we only observe with added noise." (Galit Shmueli, "Practical Time Series Forecasting: A Hands-On Guide", 2011)

"Forecasting methods attempt to isolate the systematic part and quantify the noise level. The systematic part is used for generating point forecasts and the level of noise helps assess the uncertainty associated with the point forecasts." (Galit Shmueli, "Practical Time Series Forecasting: A Hands-On Guide", 2011)

"Missing values in a time series create "holes" in the series. The presence of missing values has different implications and requires different action depending on the forecasting method." (Galit Shmueli, "Practical Time Series Forecasting: A Hands-On Guide", 2011)

"[…] noise is the random variation that results from measurement error or other causes not accounted for. It is always present in a time series to some degree, although we cannot observe it directly." (Galit Shmueli, "Practical Time Series Forecasting: A Hands-On Guide", 2011)

"Some forecasting methods directly model these components by making assumptions about their structure. For example, a popular assumption about trend is that it is linear or exponential over parts, or all, of the given time period. Another common assumption is about the noise structure: many statistical methods assume that the noise follows a normal distribution. The advantage of methods that rely on such assumptions is that when the assumptions are reasonably met, the resulting forecasts will be more robust and the models more understandable." (Galit Shmueli, "Practical Time Series Forecasting: A Hands-On Guide", 2011)

"Overfitting means that the model is not only fitting the systematic component of the data, but also the noise. An over-fitted model is therefore likely to perform poorly on new data." (Galit Shmueli, "Practical Time Series Forecasting: A Hands-On Guide", 2011)

"Understanding how performance is evaluated affects the choice of forecasting method, as well as the particular details of how a particular forecasting method is executed." (Galit Shmueli, "Practical Time Series Forecasting: A Hands-On Guide", 2011)

"When the purpose of forecasting is to generate accurate forecasts, it is useful to define performance metrics that measure predictive accuracy. Such metrics can tell us how well a particular method performs in general, as well as compared to benchmarks or forecasts from other methods." (Galit Shmueli, "Practical Time Series Forecasting: A Hands-On Guide", 2011)