Paper uses LSTM to predict inflation, finds it performs well over long periods.
problem Predicting inflation using machine learning models.
method Applies LSTM, a recurrent neural network, to forecast inflation over time.
result LSTM model performs well at long horizons and during uncertain economic times.
Inflated neural networks improve IPMN diagnosis from MRI scans.
problem Challenges in diagnosing IPMN from limited annotated data and 3D volumetric MRI.
method Developed two novel inflated deep network architectures, InceptINN and DenseINN, trained on ImageNet.
result Absolute improvement of 8.76% in accuracy for diagnosing IPMN.
A new neural network model predicts inflation and output gap more accurately.
problem Traditional Phillips curves struggle with unobserved inflation expectations and output gaps.
method Hemisphere Neural Network (HNN) that estimates latent states for inflation and output gap.
result HNN accurately forecasts inflation and identifies a large positive output gap starting from late 2020.
BiHRNN predicts inflation by leveraging hierarchical structure and bidirectional RNNs.
problem Accurate inflation forecasting is challenging due to dynamic factors and the layered structure of the Consumer Price Index.
method Bi-directional Hierarchical Recurrent Neural Network (BiHRNN) model that uses bidirectional information flow between levels and informative constraints on RNN parameters.
result BiHRNN significantly outperforms traditional RNN models in forecasting accuracy.
Study uses social network data to analyze regional inflation trends.
problem Analyzing inflation trends using social media data.
method BERT neural networks for identifying pro-inflationary and disinflationary keywords.
result Models can visualize and classify inflationary keywords in different contexts.
Study proposes a neural network approach for high inflation investment portfolios with leverage constraints.
problem Optimizing investment portfolios with high inflation and bounded leverage constraints.
method Formulated an optimal control problem, established a closed-form solution, and developed a novel LFNN approach.
result The LFNN strategy outperforms a passive benchmark by about 200 bps with a high probability of success.
Proposes a new model to predict travel demand with zero-inflated and long-tail characteristics.
problem Sparse and long-tailed travel demand data with many zeros.
method Spatial-Temporal Tweedie Graph Neural Network (STTD) using Tweedie distribution.
result STTD provides accurate predictions and precise confidence intervals.
Tornadoes are the most violent of all atmospheric storms. In a typical year, the United States experiences hundreds of tornadoes with associated damages on the order of one billion dollars. Community preparation and resilience would benefit from accurate predictions of these economic losses, particularly as populations…
ZICO learns DAGs from zero-inflated count data efficiently.
problem Learning network structures from zero-inflated count data.
method ZICO uses node-wise likelihoods with canonical links and a differentiable surrogate constraint for acyclicity.
result ZICO achieves superior performance and faster runtimes on simulated data.
A new method inflates and deflates data manifolds to estimate densities without losing universality.
problem Density estimation on low-dimensional manifolds with non-Euclidean support.
method Inflation-deflation approach using Normalizing Flows with added noise.
result Exact estimation of densities on manifolds with sufficient conditions and Gaussian noise approximation.
New model predicts travel demand uncertainty with high accuracy.
problem Uncertainty and sparsity in sparse travel demand prediction.
method Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN).
result STZINB-GNN outperforms benchmarks in predicting travel demand uncertainty.
Zero-inflated datasets, which have an excess of zero outputs, are commonly encountered in problems such as climate or rare event modelling. Conventional machine learning approaches tend to overestimate the non-zeros leading to poor performance. We propose a novel model family of zero-inflated Gaussian processes (ZiGP) …
This work explores how overparametrization and priors affect Bayesian neural network posteriors.
problem Symmetries, non-identifiabilities, and weight-space priors fragment and inflate BNN posteriors.
method We study the interplay between overparametrization and priors in BNN posteriors, deriving key phenomena and validating through experiments.
result Overparametrization induces structured, prior-aligned weight posterior distributions.
A new model improves analysis of neural activity from calcium imaging.
problem Statistical modeling of deconvolved calcium signals for neural activity interpretation.
method Proposed a zero-inflated gamma (ZIG) model to characterize calcium responses as a mixture of a gamma distribution and a point mass.
result The ZIG model outperforms simpler models in neural encoding and decoding problems.
News on inflation and monetary policy impacts US household inflation expectations.
problem Understanding how news affects inflation expectations.
method Monthly disaggregated US data from 1978 to 2016, controlling for various factors.
result News on rising inflation and easier monetary policy has a stronger impact on inflation expectations.
New L1 regularization controls neural network generalization error and sparsifies input dimensions.
problem Selecting the optimal number of hidden neurons in neural networks.
method Theoretical analysis of L1 regularization in two-layer neural networks. result Appropriate L1 regularization leads to near minimax optimal generalization risk bounds. New core inflation measure predicts future headline inflation.
problem Creating a better measure of core inflation for timely policy decisions.
method Assemblage Regression, a nonnegative ridge regression that optimizes subcomponent weights.
result Significant improvements in forecasting medium-term inflation developments.
Deep networks learn clean structure before memorizing corrupted labels, leaving a spectral signature in gradient centered scatter.
problem Deep networks' transition from learning clean structure to memorizing corrupted labels under label noise.
method Analysis of the centered scatter of per-example last-layer gradients to identify Fisher Rank Inflation.
result Fisher Rank Inflation is a spectral signature of memorization under label noise, with effective rank expanding during memorization.
Deep model tackles zero-inflated multi-species abundance estimation.
problem Predicting species distribution across landscapes with inflated zero counts.
method Proposes a novel deep learning model combining multivariate probit and log-normal distributions.
result Model outperforms existing methods on bird and fish population datasets.
This paper proposes the use of wavelet methods to estimate U.S. core inflation. It explains wavelet methods and suggests they are ideally suited to this task. Comparisons are made with traditional CPI-based and regression-based measures for their performance in following trend inflation and predicting future inflation.…
Bitcoin reacts negatively to inflation surprises, contrary to belief.
problem Bitcoin's ability to hedge inflation is questioned.
method Examined cryptocurrency responses to macroeconomic news announcements.
result Bitcoin's price decreases by 24 bps in response to inflationary surprises.
Deep artificial neural networks require a large corpus of training data in order to effectively learn, where collection of such training data is often expensive and laborious. Data augmentation overcomes this issue by artificially inflating the training set with label preserving transformations. Recently there has been…
Study finds relevance of exchange and inflation rates to economic factors.
problem Determining the relevance of exchange and inflation rates to economic factors.
method Introduced concept of adequacy, established positive relation between exchange and inflation rates and other economic factors.
result Close positive relation found between exchange and inflation rates and other economic factors.
The paper discusses the role of monetary policy when potential output depends on the inflation rate. If the intention of the central bank is to maximize actual output growth, then it has to be credibly committed to a strict inflation targeting rule, and to take the MOGIR (the Maximizing Output Growth Inflation Rate) as…
ITF improves DSR but inflates curvature, while marginal likelihood reduces it, affecting QoIs.
problem Curvature mismatch between teacher forcing and marginal likelihood in chaotic dynamical systems.
method Comparing objective-induced curvatures of ITF and marginal likelihood in a probabilistic switching augmentation of AL-RNNs.
result Curvature inflation by ITF and reduction by marginal likelihood affect dynamical quantities of interest.
The evolution of inflation, p(t), and unemployment, UE(t), in Japan has been modeled. Both variables were represented as linear functions of the change rate of labor force, dLF/LF. These models provide an accurate description of disinflation in the 1990s and a deflationary period in the 2000s. In Japan, there exists a …
New ZIPLN model accounts for zero-inflation in multivariate count data.
problem Zero-inflation in multivariate count data.
method Introduced Zero-Inflated PLN (ZIPLN) model with variational inference.
result ZIPLN significantly improves log-likelihood and reduces dispersion.
Regularized mixtures improve inflation and interest rate forecasts, especially correcting overconfidence.
problem Improving density forecasts of Eurozone inflation and real interest rates.
method Construct regularized mixtures of density forecasts with various objectives and penalties.
result Regularized mixtures outperform individual forecasters, especially correcting overconfidence.
Generative models' evaluation scores can be misleading, leading to inflated grades.
problem Misleading evaluation scores for generative models.
method Analyzed and compared various scores for evaluating synthetic vs. ground-truth data.
result The Eden score avoids grade inflation and better aligns with human perception.
Paper proposes copula-based models for analyzing multivariate zero-inflated continuous data.
problem Challenges in analyzing multivariate zero-inflated continuous data with mixed discreteness and continuity.
method Proposes two copula-based density estimation models and rectified Gaussian copula.
result Demonstrates superior performance compared to conventional methods.
We test for the long-run relationship between stock prices, inflation and its uncertainty for different U.S. sector stock indexes, over the period 2002M7 to 2015M10. For this purpose we use a cointegration analysis with one structural break to capture the crisis effect, and we assess the inflation uncertainty based on …
The paper evaluates various forecasting methods for inflation, finding ML models superior.
problem Forecasting inflation using disaggregated data and machine learning.
method Examines traditional and machine learning models, including random forest, for disaggregated and aggregated inflation forecasts.
result Aggregating disaggregated forecasts performs similarly to survey-based expectations and aggregate models.
Interest rate market models, like the LIBOR market model, have the advantage that the basic model quantities are directly observable in financial markets. Inflation market models extend this approach to inflation markets, where zero-coupon and year-on-year inflation-indexed swaps are the basic observable products. For …
A "wireless fingerprint" which exploits hardware imperfections unique to each device is a potentially powerful tool for wireless security. Such a fingerprint should be able to distinguish between devices sending the same message, and should be robust against standard spoofing techniques. Since the information in wirele…
Starting with an ideal triangulation of the interior of a compact 3-manifold M with boundary, no component of which is a 2-sphere, we provide a construction, called an inflation of the ideal triangulation, to obtain a strongly related triangulations of M itself. Besides a step-by-step algorithm for such a construction,…
We model the rate of inflation and unemployment in Austria since the early 1960s within the Phillips/Fisher framework. The change in labour force is the driving force representing economic activity in the Phillips curve. For Austria, this macroeconomic variable was first tested as a predictor of inflation and unemploym…
Paper introduces ZIPTF and C-ZIPTF for better tensor factorization of zero-inflated count data.
problem Inefficient tensor factorization for zero-inflated count data, especially in scRNA-seq.
method Zero Inflated Poisson Tensor Factorization (ZIPTF) and Consensus Zero Inflated Poisson Tensor Factorization (C-ZIPTF).
result ZIPTF and C-ZIPTF improve tensor factorization accuracy and consistency for zero-inflated count data.
The paper analyzes global inflation's systemic nature and its impact on equity markets.
problem Understanding the systemic nature of global inflation and its financial market implications.
method Data-driven study using eigenvalue analysis, inner-product optimization, and time-varying portfolio optimization.
result Countries with high centrality in global inflation are identified, and the robustness of equity indices and sectors during inflationary periods are explored.
We develop a model to price inflation and interest rates derivatives using continuous-time dynamics that have some links with macroeconomic monetary DSGE models equipped with a Taylor rule: in particular, the reaction function of the central bank, the bond market liquidity, inflation and growth expectations play an imp…
Optimal text-based indices track VIX and inflation.
problem Maximizing contemporaneous relation or predictive performance with target variables.
method Optimizing text-based indices focusing on VIX and inflation expectations.
result Superior performance compared to existing indices.
This article is an extension of the work of one of us (Coopersmith, 2011) in deriving the relationship between certain interest rates and the inflation rate of a two component economic system. We use the well-known Fisher relation between the difference of the nominal interest rate and its inflation adjusted value to e…
New model improves European inflation and interest rate predictions.
problem Improving predictions of European inflation and interest rates.
method Stochastic, continuous time model with unique solution for valuation equation.
result Model performs better on market data from 2008 to 2015.
Funds inflate their returns due to price pressure, leading to wealth reallocation and market crashes.
problem Funds inflate their returns due to price pressure, leading to wealth reallocation and market crashes.
method Decomposed fund returns into price pressure and fundamental components, and identified the impact of price chasing on fund flows.
result Funds' self-inflated returns lead to wealth reallocation and market crashes, and can be predicted by fund illiquidity.
We study the shape of inflated surfaces introduced in \cite{B1} and \cite{P1}. More precisely, we analyze profiles of surfaces obtained by inflating a convex polyhedron, or more generally an almost everywhere flat surface, with a symmetry plane. We show that such profiles are in a one-parameter family of curves which w…
This study explains and mitigates inflated returns and turnover in SPO-based portfolio optimization.
problem Inflated returns and excessive turnover in SPO-based portfolio optimization.
method KKT-based interpretation of portfolio decisions as ranking over adjusted scores, empirical evaluation of stabilization mechanisms.
result Realistic output constraints and portfolio-level turnover control improve SPO-based strategies.
R2D2-Net improves Bayesian neural networks by preventing over-shrinkage of important weights.
problem Bayesian neural networks struggle with choosing appropriate priors, leading to over-shrinkage or poor predictive performance.
method Proposes R2D2-Net with an R^2-induced Dirichlet Decomposition prior and variational Gibbs inference algorithm.
result R2D2-Net effectively shrinks irrelevant coefficients while preventing key features from over-shrinkage.
Study validates saliency maps of GNNs using selective inference.
problem Reliability of GNN saliency maps in graph-structured data.
method Statistical testing framework with selective inference to control Type I error rate.
result Valid p-values for salient subgraphs, ensuring meaningful information. In the late 90's, after severe financial and economic crisis, accompanied by inflation and exchange rate instability, Eastern Europe emerged into two groups of countries with radically contrasting monetary regimes (Currency Boards and Inflation targeting). The task of our study is to compare econometrically the perform…