Knowledge Base

The Knowledge Base 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

Confusion Matrix

Cost-Sensitive Learning

Cross-correlation

Cross Validation

D

Descriptive Analytics

E

Ensemble Methods

Equal Error Rate (EER)

Erratic Time Series

Exponential Smoothing with Covariates (ESCov)

F

Forecast Horizon

Forecasting Method

Forecast Period

G

Goodness Measure

Gradient Boosting Decision Tree Algorithm

H

Hierarchical Aggregation

Hierarchical Forecasts

I

Imbalanced Datasets

Influencing Factor

Intermittent Time Series

L

Linear Regression

Lumpy Time Series

M

Machine Learning (ML)

Mean Absolute Error (MAE)

Mean Absolute Percentage Error (MAPE)

Mean Absolute Scaled Error (MASE)

Mean Error (ME)

Mean Squared Error (MSE)

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

S

Sensitivity of a Classifier

Smooth Time Series

Specificity of a Classifier

Sporadic Time Series

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

U

Unsupervised Learning

V

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