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Tag: series

  • 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…

  • 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

  • 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

  • A Practical Toolkit for Time Series Anomaly Detection, Using Python

    A Practical Toolkit for Time Series Anomaly Detection, Using Python Here’s how to detect point anomalies within each series, and identify anomalous signals across the whole bank The post A Practical Toolkit for Time Series Anomaly Detection, Using Python appeared first on Towards Data Science. Piero Paialunga Go to original source

  • 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…

  • 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…

  • 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

  • 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…

  • 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…

  • 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…

  • 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.…

  • 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.…

  • 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…

  • 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

  • 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…

  • 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

  • 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,…