Proposes a deep learning model for probabilistic forecasting that is also interpretable.
problem Inability to explain predictions of neural network-based time series forecasting methods.
method Deep Autoregressive Networks (DANLIP) for locally interpretable probabilistic forecasting.
result DANLIP provides interpretable predictions with comparable performance to state-of-the-art methods.
Proposes a framework to explain complex global forecasting models.
problem Lack of interpretability in global forecasting models reduces stakeholder trust.
method Trains simpler univariate surrogate models on local forecasts of global models.
result Shows improved local model-agnostic interpretability of global forecasting models.
Paper explores balancing market dynamics and interpretable forecasting models for energy prices.
problem Tackles the challenge of accurately predicting mFRR price and understanding market dynamics.
method Compares XGBoost and EBM for forecasting mFRR activation price in the balancing market.
result EBM provides comparable forecasting accuracy to XGBoost but with higher interpretability.
Local surrogate model improves time series forecasts and provides interpretable explanations.
problem Improving time series forecasting accuracy while maintaining interpretability.
method A local surrogate model is used to correct the base model's predictions, making the corrections interpretable by re-fitting the base model to the error-predicted data.
result The method can discover and explain underlying patterns in the data, improving both accuracy and interpretability.
Study evaluates local explanation methods for time series forecasting.
problem Lack of local interpretability methods for multivariate time series forecasting.
method Proposed two novel evaluation metrics: Area Over the Perturbation Curve for Regression and Ablation Percentage Threshold.
result Comprehensive comparison of local explanation models on two datasets.
Graph neural networks improve El Niño forecasts.
problem Improving seasonal forecasting accuracy for El Niño-Southern Oscillation.
method Designing a novel graph connectivity learning module to model large-scale spatial interactions with ENSO forecasting.
result Our model \graphino outperforms state-of-the-art models for forecasts up to six months ahead.
Interpretable AI model boosts investment confidence and profitability.
problem Challenges in financial forecasting and interpretability in decision-making models.
method SHAP-based explainability technique for interpretable AI models.
result Notable enhancement in investor's portfolio value.
EBLR improves time series forecasting with interpretable results.
problem Forecasting future events to reduce uncertainty.
method Iterative method starting with a base model, adding regression trees to explain errors at each iteration.
result EBLR substantially improves base model performance through extracted features and provides comparable performance to other methods.
New method interprets machine learning forecasts as historical analogies.
problem Interpreting machine learning predictions as a sum of predictor contributions.
method Expressing predictions as a linear combination of in-sample values with weights based on pairwise proximity scores.
result The approach provides sparser interpretations in settings with many regressors and little training data.
SpotV2Net forecasts intraday spot volatilities using graph attention networks.
problem Forecasting multivariate intraday spot volatilities accurately.
method Graph Attention Network architecture with Fourier estimates of spot and vol-of-vol volatilities.
result SpotV2Net outperforms other models in forecasting accuracy.
The paper proposes a method to improve forecast combination accuracy using portfolio theory.
problem Improving forecast accuracy by combining multiple forecasts.
method Generates forecast combinations using a portfolio analogy, allowing negative weights for hedging.
result Demonstrates improved performance in weighted random forest forecasts.
Linear attention in Transformers can be interpreted as dynamic VAR models.
problem Misalignment between Transformers and autoregressive forecasting objectives.
method Interpreting linear attention as VAR, rearranging MLP, attention, and flow.
result SAMoVAR improves performance, interpretability, and efficiency.
The study improves Bitcoin price prediction using hybrid machine learning and enhances interpretability.
problem Improving Bitcoin price prediction accuracy and interpretability.
method Hybrid machine learning algorithms (OLS, LASSO, LSTM, decision tree regressors) and preprocessing techniques for time-series data.
result Linear regression achieves the best performance in predicting Bitcoin prices.
Graph Neural Networks improve El Niño forecasts.
problem Improving seasonal forecasting models for ENSO.
method Application of spatiotemporal Graph Neural Networks.
result Preliminary results outperform state-of-the-art systems for 1 and 3-month projections.
DCIts interprets complex time series data with interpretable coefficients.
problem Interpreting nonlinear multivariate time series data.
method Deep convolutional architecture with a Focuser and Modeler components.
result DCIts provides interpretable coefficients and interaction patterns.
Paper proposes forecast-necessity testing for accurate causal interpretation in nonlinear time-series models.
problem Misinterpretation of causal scores from nonlinear models as regression coefficients.
method Systematic edge ablation and forecast comparison to evaluate causal necessity.
result Causal relationships with similar scores can differ in their necessity for accurate prediction.
CauSTream forecasts streamflow by integrating causal graphs for better interpretability.
problem Streamflow forecasting lacks interpretability and generalization due to fixed causal models.
method CauSTream learns causal graphs for meteorological forcings and routing dependencies.
result CauSTream outperforms existing methods, especially at longer forecast windows.
Model predicts COVID-19 progression with interpretability.
problem Accurate and credible forecasting of COVID-19 progression.
method Integrates machine learning into disease modeling, uses interpretable encoders.
result More accurate forecasts than state-of-the-art alternatives.
Enhances FAVAR models with autoencoder for better economic forecasting and interpretability.
problem Limitations of linear FAVAR models in forecasting and structural analysis.
method Introduces Grouped Sparse autoencoder with time-varying parameters.
result The Grouped Sparse autoencoder produces more interpretable factors and superior forecasting performance.
RETAIN model improves glucose forecasting for diabetics, offering both accuracy and interpretability.
problem Inability of deep learning models to interpret their predictions in healthcare.
method Two-level attention mechanism in a recurrent neural network (RETAIN) architecture.
result RETAIN model achieves comparable accuracy to LSTM and FCN models while being highly interpretable.
Improved DSSMs for easier interpretable latent variables.
problem Complex and hard-to-interpret latent variables in DSSMs.
method Simplified predictive decoder and shrinkage priors.
result Interpretable latent variables improve forecasting performance.
Temporal Functional Circuits explain KAN forecasts with interpretable edge functions.
problem Lack of mechanistic explanations in KAN forecasting.
method Transform KAN edge functions into faithful, temporally grounded explanations using a gated residual KAN.
result Gated KAN achieves lower MSE than linear-only models on regime-switching signals.
The macroeconomic climate influences operations with regard to, e.g., raw material prices, financing, supply chain utilization and demand quotas. In order to adapt to the economic environment, decision-makers across the public and private sectors require accurate forecasts of the economic outlook. Existing predictive f…
NeuralProphet improves forecast accuracy by 55-92% for short-term forecasts.
problem Challenges in explainable, scalable forecasting for business decisions.
method Hybrid framework combining classical methods and deep learning, with auto-regression and covariate modules.
result NeuralProphet outperforms Prophet on real-world datasets and short-term forecasts.
Paper introduces a neural framework for accurate energy forecasting.
problem Challenges of forecasting energy demand and supply due to variability of renewable sources and dynamic consumption patterns.
method Integrates Neural ODEs, graph attention, multi-resolution wavelet transformations, and adaptive learning of frequencies.
result Consistently outperforms state-of-the-art baselines in various forecasting metrics across diverse datasets.
New framework predicts urban traffic with high accuracy.
problem Urban traffic prediction challenges.
method Interpretable attention-based neural network combining multiple modules.
result Framework outperforms state-of-the-art alternatives.
N-BEATS-MOE improves time series forecasting by adapting to series characteristics.
problem Forecasting heterogeneous time series with varying characteristics.
method Mixture-of-Experts layer with dynamic block weighting.
result Consistent improvements across 12 benchmark datasets, especially for heterogeneous series.
CMTF improves financial market forecasting by fusing multiple data types.
problem Lack of effective integration of diverse financial data sources.
method Transformer-based deep learning framework with tensor interpretation and auto-training.
result CMTF outperforms classical and deep learning models in price direction classification.
Simplifies RF predictions by focusing on a subset of nearest neighbors.
problem Improving interpretability and performance of RF-based forecast distributions.
method Sparsifying RF-based forecast distributions by focusing on a small subset of nearest neighbors.
result Simplified RF predictions can be similar to or exceed original ones in forecasting performance.
RNN(p) improves power consumption forecasts with interpretable models.
problem Improving power consumption forecasts for energy sector decisions.
method RNN(p) models with p time lags, using structured feedbacks.
result RNN(p) models achieve excellent forecasting accuracy and interpretability.
SGNNs use simulations to train neural networks, improving scientific forecasting and interpretability.
problem Combining precise theory and machine learning for robust scientific modeling.
method Pretraining neural networks on diverse mechanistic simulations as training data.
result SGNNs outperform data-driven and physics-constrained models in forecasting and interpretability.
DRN improves actuarial distributional forecasting with interpretable neural networks.
problem Challenges in modeling loss distributional properties with classic methods.
method Combines GLMs with a modified DDR method to flexibly refine baseline distribution.
result DRN improves predictive performance while maintaining interpretability.
VTA combines verbal and latent reasoning for accurate stock time-series forecasts.
problem Challenges in combining textual analysis with time-series data for financial forecasting.
method Converts stock price data into textual annotations, optimizes reasoning trace using inverse MSE, conditions time-series model outputs on reasoning attributes.
result VTA achieves state-of-the-art forecasting accuracy and interpretable reasoning traces.
Study improves exchange rate forecasting using machine learning and interpretable methods.
problem Complexity and ambiguity in financial and economic systems make precise exchange rate predictions difficult.
method Developed a fundamental-based model using machine learning and interpretability methods.
result Crude oil is the leading factor determining exchange rate dynamics, with significant events affecting its contribution.
Deep learning predicts employment changes and industry health.
problem Forecasting short-term employment changes and assessing long-term industry health.
method LSTNet, a multi-scale deep learning model, processes multivariate time series data.
result LSTNet outperforms baseline models in most sectors, especially stable ones.
Model forgets examples; this research predicts which ones to replay.
problem Language models forget examples during updates, leading to errors.
method Train forecasting models to predict which examples will be forgotten.
result Forecasting models can reduce forgetting of upstream pretraining examples.
Interpretable additive models outperform complex DL and hybrid pipelines for air quality forecasting.
problem Accurate forecasting of urban air pollution for public health and policy guidance.
method Investigated lightweight additive models (FBP, NP) vs. deep learning and hybrid pipelines on Beijing PM2.5 and PM10 data.
result Facebook Prophet consistently outperformed NeuralProphet and traditional models, achieving high R2 values. Paper distills ensemble ENSO forecasts into simpler models for better diagnostics.
problem Interpreting complex ensemble ENSO forecasts.
method Aggregates eSPA ensemble members that correctly predict ENSO phase.
result Compact distilled models maintain forecast performance and enable diagnostics.
SyMPLER improves time series forecasting in nonstationary environments with explainable models.
problem Nonstationary time series forecasting with limited interpretability.
method Dynamic piecewise-linear approximations based on Statistical Learning Theory generalization bounds.
result SyMPLER achieves comparable performance to black-box and explainable models while maintaining interpretability.
Optimized DMD for fast atmospheric chemistry forecasting.
problem Forecasting global atmospheric chemistry dynamics efficiently.
method Optimized Dynamic Mode Decomposition (DMD) for reduced order modeling.
result Significant improvement in computational speed and interpretability.
A scalable model estimates revenue uncertainty for SMEs.
problem Estimating revenue uncertainty for SMEs to manage credit limits.
method Scalable Natural Gradient Boosting Machines.
result The method distinguishes accurate from inaccurate revenue forecasts.
CVAE improves stock volume forecasting with advanced input variables.
problem Improving accuracy of daily stock volume forecasts.
method Conditional Variational Auto-Encoder (CVAE) with advanced input variables.
result CVAE generates non-linear forecasts with better accuracy and correlation to actual data.
Develops ML tool for macroeconomic forecasting with clear interpretations.
problem Forecasting and understanding macroeconomic parameters over time.
method Macroeconomic Random Forest (MRF) algorithm, Generalized Time-Varying Parameters (GTVPs).
result Clear forecasting gains and accurate predictions of unemployment and inflation.
Interpretability and stability are two important features that are desired in many contemporary big data applications arising in economics and finance. While the former is enjoyed to some extent by many existing forecasting approaches, the latter in the sense of controlling the fraction of wrongly discovered features w…
Method improves clarity in forecasting spatio-temporal data.
problem Forecasting spatio-temporal data with clarity and interpretability.
method Supervised semi-nonnegative matrix factorization with frequency regularization.
result Method offers clearer interpretability in forecasting spatio-temporal data.
OFTER predicts multivariate time series online, outperforming baselines.
problem Mid-sized multivariate time series forecasting challenges.
method k-nearest neighbors, Generalized Regression Neural Networks, dimensionality reduction.
result OFTER outperforms state-of-the-art baselines in financial multivariate time series forecasting.
Machine learning forecasts show bias at long horizons, contrary to standard tests.
problem Forecast efficiency tests misinterpret machine learning performance.
method Theoretical and empirical analysis of regularization and measurement noise.
result Machine learning forecasts exhibit overreaction at longer horizons, not bias.
Improved electricity price forecasting model combining linear and non-linear structures.
problem Day-ahead electricity price forecasting in energy systems.
method Recurrent neural networks with embedded linear structures.
result Approximately 11% higher accuracy than state-of-the-art models.