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Is ARIMA sensitive to outliers?
While the model is adept at modelling seasonality and trends, outliers are difficult to forecast for ARIMA for the very reason that they lie outside of the general trend as captured by the model.
What are the limitations of ARIMA?
Some major disadvantages of ARIMA forecasting are: first, some of the traditional model identification techniques for identifying the correct model from the class of possible models are difficult to understand and usually computationally Page 10 10 expensive.
Is missing value an outlier?
Missing values and outliers are frequently encountered during the data collection phase of observational or experimental studies conducted in all fields of natural and social sciences. When weight data are collected, a value of 250 kg cannot fit into the normal distribution for weights; it thus represents an outlier.
How to check for outliers in an ARIMA model?
Proceed with model identification and estimation as usual. Suppose this results in a tentative ARIMA model, say M. Check for additive and permanent level shifts unaccounted for by the model M by using the OUTLIER statement. In this step, unless there is evidence to justify it, the number of level shifts searched should be kept small.
Which is the default setting for X-12-ARIMA?
The default setting is SIGMA=ROBUST, which corresponds to a robust estimate suggested in an outlier detection procedure in X-12-ARIMA, the Census Bureau’s time series analysis program; see Findley et al. (1998) for additional information. The robust estimate of is computed by the formula where are the standardized residuals of the null ARIMA model.
What is an additive outlier in a time series?
Let be a regression variable that describes some type of change in the mean response. In time series literature is called a shock signature. An additive outlier at some time point corresponds to a shock signature such that and is 0.0 at all other points.
What does Arima stand for in time series?
ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average. It’s a class of models that captures a suite of different standard temporal structures in time series data.