#time-series-forecasting

How to Use a Time Series Foundation Model for Anomaly Detection

A practitioner’s guide built on Chronos-2 from AWS.

Code as the Basis Span

Tabularizing Time Series: The Foundation of ML-Based Forecasting

Most ML tutorials treat time series as just another dataset. That assumption will silently destroy your model’s performance. Here’s the…

Foundational Time Series Models might actually be a thing

Keeping Pace with LLMs: the Time Series Perspective

Transforming Energy Decision-Making Through Data: Building an End-to-End UK Electricity Demand…

Executive Summary

Time Series in Practice: The Stationarity Assumption and When It Fails

Article 4 of 6 | Infrastructure of Risk Series

Beyond Handcrafted Graphs: Learning Spatial-Temporal Dependencies for Time Series Forecasting

Imagine asking someone to predict the sequel to a novel after only letting them read the last ten pages of the first book. They might infer…

DIOSC: Disentangling Correlated Time Series with Independence-of-Support

A contrastive VAE framework for robust appliance-level energy disaggregation under real-world correlations

Forecasting with SARIMA vs Prophet: When to Use Which (and When to Give Up)

Or: One model assumes you understand math. The other assumes you understand holidays.

Can Machine Learning Help Predict Volcanic Eruptions?

This question has been central during the Ph.D. thesis of Matthieu Nougaret, defended last december at IPGP. Matthieu looked at this…

Nvidia Sharpe and Sortino ratios

In recent times

Why Time Series Forecasting Matters in the Age of AI

Artificial Intelligence is everywhere right now. From recommendation systems on shopping apps to self-driving technology, AI has slowly…

Nonlinear Feature Extraction for Financial Time Series Prediction Using Causal Legendre Polynomials

From FinTech Theory to Algorithmic Trading: Building a Complete Python Pipeline with Legendre Polynomials without Lookahead Bias 🔁🤖

Adım Adım Zaman Serisi Analizine Giriş: Zaman Serisi Nedir ve Neden Önemlidir ?

Zaman serisi analizine başlamadan önce, ilk olarak zaman serisinin ne olduğunu ve neden önemli olduğunu anlamak gerekir.

DeepFault: Depremi “Tahmin Etmek” Değil, Riski Anlamak

Sismik veriyi hava, uzay ve zamansal örüntülerle birleştirerek kısa vadeli risk yoğunluğunu modelleyen çok kaynaklı bir yapay zekâ…

ARIMA — Manual Calculations Explained

Ever felt like time-series forecasting is a bit of a “black box”? You feed data into a model, and out pops a prediction for the next month…

Time Series Data Examples: How Companies Actually Use Forecasting in 2026

Time series analysis is everywhere, from predicting energy demand to preventing flight delays. But most explanations stay abstract.

Time Series Analysis and Forecasting — Part 1: Fundamentals, Use Cases, and a Case Study from…

Hi. My name is Michał Żarnecki, and I’m a programmer specialized in data-heavy systems. Because the datasets I work with are often complex…

The Pulse of India’s Supply Chain: How Flipkart Plans for 500 Million Customers — Part 1

In the world of e-commerce, a click of a “Buy Now” button starts an invisible race. Across India (metro cities or remote villages) millions…

Forecasting Mortgage Rates with Machine Learning and Macro Scenarios

Building a data-driven mortgage rate forecasting system with probabilistic outcomes

LSTM vs. Transformer vs. CNN: Which Deep Learning Model Actually Wins for Time Series Forecasting?

I tested four popular deep learning architectures on GCash transaction data. The winner did not surprise me.

Graphs in Motion: Spatio-Temporal Dynamics with Graph Neural Networks

The concept behind ST-GNNs and a Pytorch Implementation for time series forecasting

Data Augmentation in Time Series Forecasting | Jafar Bakhshaliyev

Originally published on my blog: https://jafarbakhshaliyev.github.io/blogs/data-augmentation-time-series-forecasting.html

AI in Flood Early Warning Systems

Flood early warning sounds like a straightforward prediction problem: measure rainfall, estimate river response, and issue a warning in…

Introducing CrossLearn: Extractor-First Reinforcement Learning with Chronos-2

After multiple iterations on time-series reinforcement learning setups, a pattern became clear:

Forecasting Models That Actually Work in Production: ARIMA vs Prophet vs Bayesian (PyMC)

Every data science team eventually needs to forecast something — sales, demand, headcount, server load. And every team eventually discovers…

Time Series 13— Model Evaluation and Diagnostics

After building a time series model such as ARIMA, SARIMA, or ARMA, an important question arises:

Time Series 11 — SARIMA (Seasonal ARIMA) Theory

Many real-world time series contain seasonal patterns. These patterns repeat after a fixed interval.

ARIMA Implementation: From Theory to Production-Ready Forecasts

Part 6 of 8: ARIMA Models Part 2 — Building, Diagnosing, and Forecasting

ARIMA Models: The Mathematical Foundation of Time Series Forecasting

Part 5 of 8: Understanding AR, MA, and ARIMA (Part 1)

What is Time Series Forecasting?

Time Series Forecasting is an important area that all Machine Learning Engineers heard of. It’s often said that Time Series Forecasting…

Time Series Decomposition: When Simple Methods Break Down (And What to Do About It)

Part 2 of 8: Advanced Decomposition Techniques & STL

End to End Time Series Forecasting with SARIMAX and Prophet

Introduction: What Is Time Series Data?

Enhancing time series foundation models at Decathlon: from covariates to robust ensembling

In our previous article, The TSFM Showdown: Who’s Winning the Forecasting Battle at Decathlon?, we assessed the raw potential of Time…

Forecasting at Scale With Foundation Models: A Practical Guide

How to build a universal, end-to-end forecasting system with AutoGluon (powered with Chronos), MLflow, Luigi, and config-driven design

Eksplorasi Forecasting Penjualan Menggunakan ML.NET

Pendahuluan

Time-Series Forecasting Made Simple: A Practical Introduction

Forecasting is a technique to make an estimate of what will happen tomorrow, next quarter or next year. From a business point of you…

The TSFM Showdown: Who’s Winning the Forecasting Battle at Decathlon?

In Decathlon, automatic forecasting plays a crucial role in our daily operations: demand forecasting, dispatch, turnover prediction…

Data Science Fundamentals: Working with Time Series Data

Humans seemed to have an innate desire explain the future since the dawn of time. From early agriculture societies that used past…

Chronos: Pretrained Models for Time Series Forecasting

From “language of time series” to universal zero-shot forecasting — how Amazon Science re-imagines time-series modeling

Exponential Smoothing for Time Series Forecasting

Predict the future

What is the difference between the ARIMA and the Holt Winters forecasting model?

In the past several blog posts I have covered time series analysis and then time series forecasting on R’s inbuilt datasets. Two main…

Use R’s SARIMA model to forecast quarterly earnings per share

In my last blog post I carried out an ARIMA on the R’s inbuilt JohnsonJohnson dataset, and that post can be read here…

Apart Research Hackathon — AI Incidents Forecasting

From an Apart Research hackathon, we used the AI Incidents Database to forecast that reported AI harms could grow 6–11× by 2030, with misuse

From LLMs to Time Series — The Next Wave of AI Foundation Models

While LLMs and AI Agents are stealing the spotlight and being deployed in real-world business use cases, the underlying model architecture…

Forecasting the Future: Time-Series Analysis with SARIMAX and Volatility Modeling

Time-series analysis is a tool that quietly powers a lot of the predictions we rely on every day. At its core, it uses patterns in past…

Predicting the Rhythm of Business: Building a Sales Forecasting Model

A step-by-step guide to building a Random Forest model that can predict future retail sales.

Time Series Forecasting Lesson 1: Exponential Smoothing

Time Series Forecasting:

Time Series Forecasting

In this article, I will explain the basics of Time Series Forecasting and demonstrate, how we can implement various forecasting models in…

Machine Learning for Time Series: Building Workflows That Scale

Focus on pipelines and reproducibility across many series.