Forecasting Wiki

The Forecasting Wiki explains key concepts in predictive analytics and data science, provides methodological knowledge, and promotes a deeper understanding of forecasting, making results more comprehensible.

A

Absolute Error (AE)

Absolute Percentage Error (APE)

Accuracy of a Classifier

Area Under the Curve (AUC)

Artificial Neural Network (ANN)

Autocorrelation

Autoregressive Integrated Moving Average (ARIMA)

B

Backtesting

Binary Classification

C

Classification of Time Series

Classification Threshold

Convolutional Neural Networks (CNNs)

Confusion Matrix

Cost-Sensitive Learning

Cross-correlation

Cross Validation

D

Descriptive Analytics

Deterministic Trend

E

Ensemble Methods

Equal Error Rate (EER)

Erratic Time Series

Exponential Smoothing with Covariates (ESCov)

Exponential Trend

F

Forecast Horizon

Forecasting Methods

Forecast Period

Foundation Model

Fourier Transformation

G

Graph Neural Networks (GNNs)

Goodness Measure

Gradient Boosting Decision Tree Algorithm

H

Hierarchical Aggregation

Hierarchical Forecasts

I

Imbalanced Datasets

Influencing Factor

Intermittent Time Series

L

Large Language Models (LLMs)

Linear Regression

Lumpy Time Series

M

Machine Learning (ML)

Machine Learning Methods

Mean Absolute Error (MAE)

Mean Absolute Percentage Error (MAPE)

Mean Absolute Scaled Error (MASE)

Mean Error (ME)

Mean Squared Error (MSE)

Multi-Layer Perceptrons (MLPs)

Model Selection

Model Selection and Validation

Multi-Class Classification

Multivariate Adaptive Regression Splines (MARS)

N

O

Out-of-sample Validation

P

Percentage Error (PE)

Periods in Stock (PIS)

Point Forecasts

Precision of a Classifier

Predictive Analytics

Prescriptive Analytics

Prediction Confidence Level

Prediction Interval

R

Receiver Operating Characteristic (ROC)

Regularized Regression

Recurrent Neural Networks (RNNs)

S

Seasonal and Trend Loess (STL)

Sensitivity of a Classifier

Smooth Time Series

Specificity of a Classifier

Sporadic Time Series

Stochastic Trend

Supervised Learning

Support Vector Machine (SVM)

Symmetric MAPE (sMAPE)

T

Temporal Aggregation

Teunter-Syntetos-Babai Method (TSB)

Time Series Forecasting

Time Series Hierarchies

Training and Validation

Transformer Architecture

U

Unsupervised Learning

V

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