Time-Series Numeric Prediction Task

Context:
- Input: a Timeseries Dataset (typically an unlabeled timeseries dataset).
- output: a List of Point Estimates.
  - optional: a Forecasting Model.
- Performance Measure: Forecasting Performance Measure.
- It can be solved by a Forecasting System (that implements a forecasting algorithm).
- It can range from being a Univariate Forecasting Task to being a Multi-Predictor Forecasting Task.
- It can support a Sequential-Data Data Mining Task.
- It can be supported by an Autocorrelation Task.
Example(s)
- a Daily Rain Quantity Forecasting Task (within weather forecasting).
- an M-Competition such as the M3-Competition Benchmark Task.
- Monthly Sea Surface Temperature Prediction, http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/lanina/ensoforecast.shtml
- an Economic Forecasting Task, such as an EPC prediction task.
- a National Quarterly GDP Forecasting Task (of national GDP).
- …
Counter-Example(s):
- a Time Series Classification Task.
- a Time Series Ranking Task.
See: What-If Analysis, Timeseries Analysis Task, IID Point Estimation Task.

References

(Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Forecasting Retrieved:2020-4-28.
- Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods. Usage can differ between areas of application: for example, in hydrology the terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specific future times, while the term "prediction" is used for more general estimates, such as the number of times floods will occur over a long period.
  Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible. In some cases the data used to predict the variable of interest is itself forecast.

http://en.wikipedia.org/wiki/Time_series#Analysis
- Fully formed statistical models for stochastic simulation purposes, so as to generate alternative versions of the time series, representing what might happen over non-specific time-periods in the future
- Simple or fully formed statistical models to describe the likely outcome of the time series in the immediate future, given knowledge of the most recent outcomes (forecasting).
- Forecasting on time series is usually done using automated statistical software packages and programming languages, such as R (programming language), S (programming language), SAS (software), SPSS, Minitab and many others.

(Hyndman & Koehler, 2006) ⇒ Rob J. Hyndman, and A. B. Koehler. (2006). “Another Look at Measures of Forecast Accuracy.” In: International Journal of Forecasting, 22(4). [doi>10.1016/j.ijforecast.2006.03.001
- We discuss and compare measures of accuracy of univariate time series forecasts. The methods used in the M-competition as well as the M3-competition, and many of the measures recommended by previous authors on this topic, are found to be degenerate in commonly occurring situations. Instead, we propose that the mean absolute scaled error become the standard measure for comparing forecast accuracy across multiple time series.

(Murphy, 1993) ⇒ Allan H. Murphy. (1993). “What is a Good Forecast? An essay on the nature of goodness in weather forecasting.” In: Weather and Forecasting, 8(2). 1993)008<0281:WIAGFA>2.0.CO;2 doi:10.1175/1520-0434(1993)008<0281:WIAGFA>2.0.CO;2