Everything about Forecasting
Making predictions with available data
Forecasting is the process of making predictions based on past and present data. These forecasts can later be compared with actual outcomes. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis. Prediction is a similar but more general term. Forecasting might refer to specific formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods or the process of prediction and assessment of its accuracy. Usage can vary 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. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: ForecastingA 5-minute orientation
- Skim a recent paper: Are Transformers Effective for Time Series Forecasting?Influential work since 2022
- Pick up a classic book: Meteorology todayThe most-reprinted book on the subject
- Read the landmark paper: River flow forecasting through conceptual models part I — A discussion of principlesCited 19,910 times
- Go sideways: Demand forecastingA closely related topic
- Go sideways: Mean absolute scaled errorA closely related topic
📖 Definitions
Verb present participle and gerund of forecast
Noun A forecast or prediction.
🏆 Landmark papers (most cited)
River flow forecasting through conceptual models part I — A discussion of principles
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Forecasting Volatility in Financial Markets: A Review
Time series forecasting using a hybrid ARIMA and neural network model
Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study
Forecasting with artificial neural networks:
Ensemble forecasting of species distributions
Temporal Fusion Transformers for interpretable multi-horizon time series forecasting
🆕 Influential since 2022
Are Transformers Effective for Time Series Forecasting?
Accurate medium-range global weather forecasting with 3D neural networks
Learning skillful medium-range global weather forecasting
Graph neural network for traffic forecasting: A survey
Global, regional, and national prevalence of adult overweight and obesity, 1990–2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021
📚 Books worth reading
Meteorology todayC. Donald Ahrens · 1982
Profiles of the futureArthur C. Clarke · 1962
Job, a comedy of justiceRobert A. Heinlein · 1795
SuperforecastingPhilip E. Tetlock, Dan Gardner · 2015
Forecasting financial marketsTony Plummer · 1989
Business forecastingJohn E. Hanke, Dean Wichern · 1981
Stumbling on happinessDaniel T. Gilbert · 2006
SpilloverDavid Quammen, Cécile Dutheil de La Rochère · 2012
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Demand forecasting Demand forecasting, also known as demand planning and sales forecasting (DP&SF), involves the prediction of the quantity of goods and services that will…
- Mean absolute scaled error In statistics, the mean absolute scaled error (MASE) is a measure of the accuracy of forecasts. It is the mean absolute error of the forecast values, divided…
- Autoregressive integrated moving average In time series analysis used in statistics and econometrics, autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) models are generalizat…
- Root mean square deviation The root mean square deviation (RMSD) or root mean square error (RMSE) is a frequently used measure of the distances between actual observed values and…
- Seasonality In time series data, seasonality refers to the trends that occur at specific regular intervals less than a year, such as weekly, monthly, or quarterly…
- Nonhomogeneous Gaussian regression Non-homogeneous Gaussian regression (NGR) is a type of statistical regression analysis used in the atmospheric sciences as a way to convert ensemble forecasts…
- Mean absolute percentage error The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), is a measure of prediction accuracy of a forecasting…
- Telecommunications forecasting Telecommunications service providers perform forecasting calculations to assist them in planning their networks. Accurate forecasting helps operators to…