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Tag: time

  • Time-Aware Latent Space Bayesian Optimization

    Time-Aware Latent Space Bayesian Optimization arXiv:2603.00935v1 Announce Type: new Abstract: Latent-space Bayesian optimization (LSBO) extends Bayesian optimization to structured domains, such as molecular design, by searching in the continuous latent space of a generative model. However, most LSBO methods assume a fixed objective, whereas real design campaigns often face temporal drift (e.g., evolving preferences or…

  • So what do y’all think of the Block layoffs?

    So what do y’all think of the Block layoffs? My upcoming interview with Block got canceled, and I am in a bit of relief but at the same time it made me question where is the industry in general headed to. Block CEO is attributing the layoffs to AI. As an active job seeker and…

  • Time Series Themed Children’s Book

    Time Series Themed Children’s Book For the parents out there’s looking to share the joys of data collection, cleaning, time series modeling, and forecasting error with their little ones. Written completely in rhyme and all about using data to solve problems. Alternatively, Harry’s Lemonade Solution could be used to teach your parents a little bit…

  • Amortized Bayesian inference for actigraph time sheet data from mobile devices

    Amortized Bayesian inference for actigraph time sheet data from mobile devices arXiv:2602.20611v1 Announce Type: new Abstract: Mobile data technologies use “actigraphs” to furnish information on health variables as a function of a subject’s movement. The advent of wearable devices and related technologies has propelled the creation of health databases consisting of human movement data to…

  • Logarithmic-time Schedules for Scaling Language Models with Momentum

    Logarithmic-time Schedules for Scaling Language Models with Momentum arXiv:2602.05298v1 Announce Type: new Abstract: In practice, the hyperparameters $(beta_1, beta_2)$ and weight-decay $lambda$ in AdamW are typically kept at fixed values. Is there any reason to do otherwise? We show that for large-scale language model training, the answer is yes: by exploiting the power-law structure of…

  • Rethinking Test-Time Training: Tilting The Latent Distribution For Few-Shot Source-Free Adaptation

    Rethinking Test-Time Training: Tilting The Latent Distribution For Few-Shot Source-Free Adaptation arXiv:2602.02633v1 Announce Type: new Abstract: Often, constraints arise in deployment settings where even lightweight parameter updates e.g. parameter-efficient fine-tuning could induce model shift or tuning instability. We study test-time adaptation of foundation models for few-shot classification under a completely frozen-model regime, where additionally, no…

  • Does Calendar-Based Time-Intelligence Change Custom Logic?

    Does Calendar-Based Time-Intelligence Change Custom Logic? Let’s look at calculating the moving average over time The post Does Calendar-Based Time-Intelligence Change Custom Logic? appeared first on Towards Data Science. Salvatore Cagliari Go to original source

  • Time Series Isn’t Enough: How Graph Neural Networks Change Demand Forecasting

    Time Series Isn’t Enough: How Graph Neural Networks Change Demand Forecasting Why modeling SKUs as a network reveals what traditional forecasts miss The post Time Series Isn’t Enough: How Graph Neural Networks Change Demand Forecasting appeared first on Towards Data Science. Partha Sarkar Go to original source

  • TDS Newsletter: Is It Time to Revisit RAG?

    TDS Newsletter: Is It Time to Revisit RAG? Let’s make sense of the current state of retrieval-augmented generation The post TDS Newsletter: Is It Time to Revisit RAG? appeared first on Towards Data Science. TDS Editors Go to original source

  • Retrieval for Time-Series: How Looking Back Improves Forecasts

    Retrieval for Time-Series: How Looking Back Improves Forecasts Why Retrieval Helps in Time Series Forecasting We all know how it goes: Time-series data is tricky. Traditional forecasting models are unprepared for incidents like sudden market crashes, black swan events, or rare weather patterns. Even large fancy models like Chronos sometimes struggle because they haven’t dealt…

  • EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas

    EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas Learn how to analyze product performance, extract time-series features, and uncover key seasonal trends in your sales data. The post EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas appeared first on Towards Data Science. Ibrahim Salami Go to original source

  • No-Regret Gaussian Process Optimization of Time-Varying Functions

    No-Regret Gaussian Process Optimization of Time-Varying Functions arXiv:2512.00517v1 Announce Type: new Abstract: Sequential optimization of black-box functions from noisy evaluations has been widely studied, with Gaussian Process bandit algorithms such as GP-UCB guaranteeing no-regret in stationary settings. However, for time-varying objectives, it is known that no-regret is unattainable under pure bandit feedback unless strong and…

  • Time dependent loss reweighting for flow matching and diffusion models is theoretically justified

    Time dependent loss reweighting for flow matching and diffusion models is theoretically justified arXiv:2511.16599v1 Announce Type: new Abstract: This brief note clarifies that, in Generator Matching (which subsumes a large family of flow matching and diffusion models over continuous, manifold, and discrete spaces), both the Bregman divergence loss and the linear parameterization of the generator…

  • Data Visualization Explained (Part 5): Visualizing Time-Series Data in Python (Matplotlib, Plotly, and Altair)

    Data Visualization Explained (Part 5): Visualizing Time-Series Data in Python (Matplotlib, Plotly, and Altair) An explanation of time-series visualization, including in-depth code examples in Matplotlib, Plotly, and Altair. The post Data Visualization Explained (Part 5): Visualizing Time-Series Data in Python (Matplotlib, Plotly, and Altair) appeared first on Towards Data Science. Murtaza Ali Go to original…

  • Introducing ShaTS: A Shapley-Based Method for Time-Series Models

    Introducing ShaTS: A Shapley-Based Method for Time-Series Models Why you should not explain your time-series data with tabular Shapley methods The post Introducing ShaTS: A Shapley-Based Method for Time-Series Models appeared first on Towards Data Science. Manuel Franco de la Peña Go to original source

  • How to Decide Between Regression and Time Series Models for “Forecasting”?

    How to Decide Between Regression and Time Series Models for “Forecasting”? Hi everyone, I’m trying to understand intuitively when it makes sense to use a time series model like SARIMAX versus a simpler approach like linear regression, especially in cases of weak autocorrelation. For example, in wind power generation forecasting, energy output mainly depends on…

  • LLM-Powered Time-Series Analysis

    LLM-Powered Time-Series Analysis Part 2: Prompts for Advanced Model Development The post LLM-Powered Time-Series Analysis appeared first on Towards Data Science. Sara Nobrega Go to original source

  • Learning Time-Varying Graphs from Incomplete Graph Signals

    Learning Time-Varying Graphs from Incomplete Graph Signals arXiv:2510.17903v1 Announce Type: new Abstract: This paper tackles the challenging problem of jointly inferring time-varying network topologies and imputing missing data from partially observed graph signals. We propose a unified non-convex optimization framework to simultaneously recover a sequence of graph Laplacian matrices while reconstructing the unobserved signal entries.…

  • Transformers, Time Series, and the Myth of Permutation Invariance

    Transformers, Time Series, and the Myth of Permutation Invariance There’s a common misconception in ML/DL that Transformers shouldn’t be used for forecasting because attention is permutation-invariant. Latest evidence shows the opposite, such as Google’s latest model, where the experiments show the model performs just as well with or without positional embeddings. You can find an…

  • Prompt Engineering for Time-Series Analysis with Large Language Models

    Prompt Engineering for Time-Series Analysis with Large Language Models Part 1: Prompts for Core Strategies in Time-Series The post Prompt Engineering for Time-Series Analysis with Large Language Models appeared first on Towards Data Science. Sara Nobrega Go to original source

  • Bayesian Nonparametric Dynamical Clustering of Time Series

    Bayesian Nonparametric Dynamical Clustering of Time Series arXiv:2510.06919v1 Announce Type: new Abstract: We present a method that models the evolution of an unbounded number of time series clusters by switching among an unknown number of regimes with linear dynamics. We develop a Bayesian non-parametric approach using a hierarchical Dirichlet process as a prior on the…

  • Predictive inference for time series: why is split conformal effective despite temporal dependence?

    Predictive inference for time series: why is split conformal effective despite temporal dependence? arXiv:2510.02471v1 Announce Type: new Abstract: We consider the problem of uncertainty quantification for prediction in a time series: if we use past data to forecast the next time point, can we provide valid prediction intervals around our forecasts? To avoid placing distributional…

  • Real-Time Intelligence in Microsoft Fabric: The Ultimate Guide

    Real-Time Intelligence in Microsoft Fabric: The Ultimate Guide Once upon a time, handling streaming data was considered an avant-garde approach. Since the introduction of relational database management systems in the 1970s and traditional data warehousing systems in the late 1980s, all data workloads began and ended with the so-called batch processing. Batch processing relies on the concept of…

  • AI Foundation Model for Time Series with Innovations Representation

    AI Foundation Model for Time Series with Innovations Representation arXiv:2510.01560v1 Announce Type: new Abstract: This paper introduces an Artificial Intelligence (AI) foundation model for time series in engineering applications, where causal operations are required for real-time monitoring and control. Since engineering time series are governed by physical, rather than linguistic, laws, large-language-model-based AI foundation models…

  • Hands On Time Series Modeling of Rare Events, with Python

    Hands On Time Series Modeling of Rare Events, with Python This is how to model rare events occurrences in a time series in a few lines of code The post Hands On Time Series Modeling of Rare Events, with Python appeared first on Towards Data Science. Piero Paialunga Go to original source

  • Time Series Forecasting Made Simple (Part 4.1): Understanding Stationarity in a Time Series

    Time Series Forecasting Made Simple (Part 4.1): Understanding Stationarity in a Time Series An intuitive guide to stationarity in a time series The post Time Series Forecasting Made Simple (Part 4.1): Understanding Stationarity in a Time Series appeared first on Towards Data Science. Nikhil Dasari Go to original source

  • Scalable h-adaptive probabilistic solver for time-independent and time-dependent systems

    Scalable h-adaptive probabilistic solver for time-independent and time-dependent systems arXiv:2508.09623v1 Announce Type: new Abstract: Solving partial differential equations (PDEs) within the framework of probabilistic numerics offers a principled approach to quantifying epistemic uncertainty arising from discretization. By leveraging Gaussian process regression and imposing the governing PDE as a constraint at a finite set of collocation…

  • Time Series Forecasting Made Simple (Part 3.2): A Deep Dive into LOESS-Based Smoothing

    Time Series Forecasting Made Simple (Part 3.2): A Deep Dive into LOESS-Based Smoothing Explore how STL uses LOESS smoothing to extract trend and seasonal components. The post Time Series Forecasting Made Simple (Part 3.2): A Deep Dive into LOESS-Based Smoothing appeared first on Towards Data Science. Nikhil Dasari Go to original source

  • Bayesian Modeling and Estimation of Linear Time-Variant Systems using Neural Networks and Gaussian Processes

    Bayesian Modeling and Estimation of Linear Time-Variant Systems using Neural Networks and Gaussian Processes arXiv:2507.12878v1 Announce Type: new Abstract: The identification of Linear Time-Variant (LTV) systems from input-output data is a fundamental yet challenging ill-posed inverse problem. This work introduces a unified Bayesian framework that models the system’s impulse response, $h(t, tau)$, as a stochastic…

  • Toto: A Foundation Time-Series Model Optimized for Observability Data

    Toto: A Foundation Time-Series Model Optimized for Observability Data Datadog open-sourced Toto (Time Series Optimized Transformer for Observability), a model purpose-built for observability data. Toto is currently the most extensively pretrained time-series foundation model: The pretraining corpus contains 2.36 trillion tokens, with ~70% coming from Datadog’s private telemetry dataset. Also, Toto currently ranks 2nd in…

  • Time-dependent density estimation using binary classifiers

    Time-dependent density estimation using binary classifiers arXiv:2506.15505v1 Announce Type: new Abstract: We propose a data-driven method to learn the time-dependent probability density of a multivariate stochastic process from sample paths, assuming that the initial probability density is known and can be evaluated. Our method uses a novel time-dependent binary classifier trained using a contrastive estimation-based…

  • Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation

    Temporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation arXiv:2506.12183v1 Announce Type: new Abstract: Evaluating anomaly detection in multivariate time series (MTS) requires careful consideration of temporal dependencies, particularly when detecting subsequence anomalies common in fault detection scenarios. While time series cross-validation (TSCV) techniques aim to preserve temporal ordering during model evaluation, their impact…

  • Online Conformal Model Selection for Nonstationary Time Series

    Online Conformal Model Selection for Nonstationary Time Series arXiv:2506.05544v1 Announce Type: new Abstract: This paper introduces the MPS (Model Prediction Set), a novel framework for online model selection for nonstationary time series. Classical model selection methods, such as information criteria and cross-validation, rely heavily on the stationarity assumption and often fail in dynamic environments which…

  • Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series

    Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series arXiv:2506.05354v1 Announce Type: cross Abstract: Nonstationarity of real-life time series requires model adaptation. In classical approaches like ARMA-ARCH there is assumed some arbitrarily chosen dependence type. To avoid their bias, we will focus on novel more agnostic approach: moving…

  • Hands-On Attention Mechanism for Time Series Classification, with Python

    Hands-On Attention Mechanism for Time Series Classification, with Python This is how to use the attention mechanism in a time series classification framework The post Hands-On Attention Mechanism for Time Series Classification, with Python appeared first on Towards Data Science. Piero Paialunga Go to original source

  • Operator Learning for Schr”{o}dinger Equation: Unitarity, Error Bounds, and Time Generalization

    Operator Learning for Schr”{o}dinger Equation: Unitarity, Error Bounds, and Time Generalization arXiv:2505.18288v1 Announce Type: new Abstract: We consider the problem of learning the evolution operator for the time-dependent Schr”{o}dinger equation, where the Hamiltonian may vary with time. Existing neural network-based surrogates often ignore fundamental properties of the Schr”{o}dinger equation, such as linearity and unitarity, and…

  • An Efficient Transport-Based Dissimilarity Measure for Time Series Classification under Warping Distortions

    An Efficient Transport-Based Dissimilarity Measure for Time Series Classification under Warping Distortions arXiv:2505.05676v1 Announce Type: cross Abstract: Time Series Classification (TSC) is an important problem with numerous applications in science and technology. Dissimilarity-based approaches, such as Dynamic Time Warping (DTW), are classical methods for distinguishing time series when time deformations are confounding information. In this…

  • Clustering Eating Behaviors in Time: A Machine Learning Approach to Preventive Health

    Clustering Eating Behaviors in Time: A Machine Learning Approach to Preventive Health It’s well known that what we eat matters — but what if when and how often we eat matters just as much? In the midst of ongoing scientific debate around the benefits of intermittent fasting, this question becomes even more intriguing. As someone passionate about machine learning and healthy living,…

  • TV-SurvCaus: Dynamic Representation Balancing for Causal Survival Analysis

    TV-SurvCaus: Dynamic Representation Balancing for Causal Survival Analysis arXiv:2505.01785v1 Announce Type: new Abstract: Estimating the causal effect of time-varying treatments on survival outcomes is a challenging task in many domains, particularly in medicine where treatment protocols adapt over time. While recent advances in representation learning have improved causal inference for static treatments, extending these methods…

  • Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics

    Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics arXiv:2504.20099v1 Announce Type: cross Abstract: The present study explores the interpretability of latent spaces produced by time series foundation models, focusing on their potential for visual analysis tasks. Specifically, we evaluate the MOMENT family of models, a set of transformer-based, pre-trained…

  • Optimal Scheduling of Dynamic Transport

    Optimal Scheduling of Dynamic Transport arXiv:2504.14425v1 Announce Type: new Abstract: Flow-based methods for sampling and generative modeling use continuous-time dynamical systems to represent a {transport map} that pushes forward a source measure to a target measure. The introduction of a time axis provides considerable design freedom, and a central question is how to exploit this…

  • Time Series Forecasting Made Simple (Part 1): Decomposition and Baseline Models

    Time Series Forecasting Made Simple (Part 1): Decomposition and Baseline Models I used to avoid time series analysis. Every time I took an online course, I’d see a module titled “Time Series Analysis” with subtopics like Fourier Transforms, autocorrelation functions and other intimidating terms. I don’t know why, but I always found a reason to avoid…

  • Mamba time series forecasting with uncertainty propagation

    Mamba time series forecasting with uncertainty propagation arXiv:2503.10873v1 Announce Type: new Abstract: State space models, such as Mamba, have recently garnered attention in time series forecasting due to their ability to capture sequence patterns. However, in electricity consumption benchmarks, Mamba forecasts exhibit a mean error of approximately 8%. Similarly, in traffic occupancy benchmarks, the mean…

  • Linear Regression in Time Series: Sources of Spurious Regression

    Linear Regression in Time Series: Sources of Spurious Regression 1. Introduction It’s pretty clear that most of our work will be automated by AI in the future. This will be possible because many researchers and professionals are working hard to make their work available online. These contributions not only help us understand fundamental concepts but…

  • Boltzmann convolutions and Welford mean-variance layers with an application to time series forecasting and classification

    Boltzmann convolutions and Welford mean-variance layers with an application to time series forecasting and classification arXiv:2503.04956v1 Announce Type: new Abstract: In this paper we propose a novel problem called the ForeClassing problem where the loss of a classification decision is only observed at a future time point after the classification decision has to be made.…

  • Time-varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder

    Time-varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder arXiv:2503.04386v1 Announce Type: new Abstract: Recent economic events, including the global financial crisis and COVID-19 pandemic, have exposed limitations in linear Factor Augmented Vector Autoregressive (FAVAR) models for forecasting and structural analysis. Nonlinear dimension techniques, particularly autoencoders, have emerged as promising alternatives in a FAVAR framework,…

  • Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood

    Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood arXiv:2502.19086v1 Announce Type: new Abstract: We introduce the use of Gaussian Processes (GPs) for the probabilistic forecasting of intermittent time series. The model is trained in a Bayesian framework that accounts for the uncertainty about the latent function and marginalizes it out when making predictions.…

  • Advanced Time Intelligence in DAX with Performance in Mind

    Advanced Time Intelligence in DAX with Performance in Mind We all know the usual Time Intelligence function based on years, quarters, months, and days. But sometimes, we need to perform more exotic timer intelligence calculations. But we should not forget to consider performance while programming the measures.  Introduction  There are many Dax functions in Power BI…

  • Forecasting time series with constraints

    Forecasting time series with constraints arXiv:2502.10485v1 Announce Type: new Abstract: Time series forecasting presents unique challenges that limit the effectiveness of traditional machine learning algorithms. To address these limitations, various approaches have incorporated linear constraints into learning algorithms, such as generalized additive models and hierarchical forecasting. In this paper, we propose a unified framework for…

  • Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions

    Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions arXiv:2502.00302v1 Announce Type: new Abstract: How can we identify groups of primate individuals which could be conjectured to drive social structure? To address this question, one of us has collected a time series of data for social interactions between chimpanzees. Here we…

  • Optimizing Through Change: Bounds and Recommendations for Time-Varying Bayesian Optimization Algorithms

    Optimizing Through Change: Bounds and Recommendations for Time-Varying Bayesian Optimization Algorithms arXiv:2501.18963v1 Announce Type: new Abstract: Time-Varying Bayesian Optimization (TVBO) is the go-to framework for optimizing a time-varying, expensive, noisy black-box function. However, most of the solutions proposed so far either rely on unrealistic assumptions on the nature of the objective function or do not…

  • A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges

    A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges arXiv:2501.15196v1 Announce Type: new Abstract: Time series anomaly detection presents various challenges due to the sequential and dynamic nature of time-dependent data. Traditional unsupervised methods frequently encounter difficulties in generalization, often overfitting to known normal patterns observed during training and…

  • Influential Time-Series Forecasting Papers of 2023-2024: Part 1

    Influential Time-Series Forecasting Papers of 2023-2024: Part 1 This article explores some of the latest advancements in time-series forecasting. You can find the article here. Edit: If you know of any other interesting papers, please share them in the comments. submitted by /u/nkafr [link] [comments] /u/nkafr Go to original source

  • How To: Forecast Time Series Using Lags

    How To: Forecast Time Series Using Lags Lag columns can significantly boost your model’s performance Continue reading on Towards Data Science » Haden Pelletier Go to original source

  • Dynamic Causal Structure Discovery and Causal Effect Estimation

    Dynamic Causal Structure Discovery and Causal Effect Estimation arXiv:2501.06534v1 Announce Type: new Abstract: To represent the causal relationships between variables, a directed acyclic graph (DAG) is widely utilized in many areas, such as social sciences, epidemics, and genetics. Many causal structure learning approaches are developed to learn the hidden causal structure utilizing deep-learning approaches. However,…

  • Missing Data in Time-Series? Machine Learning Techniques (Part 2)

    Missing Data in Time-Series? Machine Learning Techniques (Part 2) Using Clustering Algorithms to Handle Missing Time-Series Data Continue reading on Towards Data Science » Sara Nóbrega Go to original source

  • What’s your biggest time sink as a data scientist?

    What’s your biggest time sink as a data scientist? I’ve got a few ideas for DS tooling I was thinking of taking on as a side project, so this is a bit of a market research post. I’m curious what data-scientist specific task/problem is the biggest time suck for you at work. I feel like…

  • Stop the Count! Why Putting A Time Limit on Metrics is Critical for Fast and Accurate Experiments

    Stop the Count! Why Putting A Time Limit on Metrics is Critical for Fast and Accurate Experiments Why your experiments might never reach significance Photo by Andrik Langfield on Unsplash Introduction Experiments usually compare the frequency of an event (or some other sum metric) after either exposure (treatment) or non-exposure (control) to some intervention. For example:…

  • Surrogate Modeling for Explainable Predictive Time Series Corrections

    Surrogate Modeling for Explainable Predictive Time Series Corrections arXiv:2412.19897v1 Announce Type: new Abstract: We introduce a local surrogate approach for explainable time-series forecasting. An initially non-interpretable predictive model to improve the forecast of a classical time-series ‘base model’ is used. ‘Explainability’ of the correction is provided by fitting the base model again to the data…

  • Enhancing Masked Time-Series Modeling via Dropping Patches

    Enhancing Masked Time-Series Modeling via Dropping Patches arXiv:2412.15315v1 Announce Type: new Abstract: This paper explores how to enhance existing masked time-series modeling by randomly dropping sub-sequence level patches of time series. On this basis, a simple yet effective method named DropPatch is proposed, which has two remarkable advantages: 1) It improves the pre-training efficiency by…

  • Using matrix-product states for time-series machine learning

    Using matrix-product states for time-series machine learning arXiv:2412.15826v1 Announce Type: new Abstract: Matrix-product states (MPS) have proven to be a versatile ansatz for modeling quantum many-body physics. For many applications, and particularly in one-dimension, they capture relevant quantum correlations in many-body wavefunctions while remaining tractable to store and manipulate on a classical computer. This has…

  • Time-Reversible Bridges of Data with Machine Learning

    Time-Reversible Bridges of Data with Machine Learning arXiv:2412.13665v1 Announce Type: new Abstract: The analysis of dynamical systems is a fundamental tool in the natural sciences and engineering. It is used to understand the evolution of systems as large as entire galaxies and as small as individual molecules. With predefined conditions on the evolution of dy-namical…

  • Missing Data in Time-Series: Machine Learning Techniques

    Missing Data in Time-Series: Machine Learning Techniques Part 1: Leverage linear regression and decision trees to impute time-series gaps. Continue reading on Towards Data Science » Sara Nóbrega Go to original source

  • Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application

    Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application arXiv:2412.05906v1 Announce Type: new Abstract: We study the discrete-time linear-quadratic (LQ) control model using reinforcement learning (RL). Using entropy to measure the cost of exploration, we prove that the optimal feedback policy for the problem must be Gaussian type. Then, we apply the results…

  • Uncertainty Quantification in Time Series Forecasting

    Uncertainty Quantification in Time Series Forecasting A deep dive into EnbPI, a Conformal Prediction approach for time series forecasting Continue reading on Towards Data Science » Jonte Dancker Go to original source

  • Daily averaged time series comparison -Linking plankton and aerosols emissions?

    Daily averaged time series comparison -Linking plankton and aerosols emissions? Hi everyone, so we have this dataset of daily averaged pytoplankton time series over a full year; coccolithophores, chlorophytes, cyanobacteria, diatoms, dinoflagellates, phaecocystis, zooplankton. Then we have atmospheric measurements on the same time intervals of a few aerosols species; Methanesulphonic acid, carboxylic acids, aliphatics, sulphates,…