Time series work is where modelling assumptions meet time, operations and data collection. This hub emphasizes forecasting decisions, validation, seasonality, anomalies and the practical traps that make backtests too optimistic.
Start here
- Forecasting Baselines That Are Hard to Beat
- Feature Engineering for Time Series Without Leaking the Future
- Missing Data and Irregular Sampling in Time Series
- State Space Models and the Kalman Filter
Forecasting practice
Baselines, probabilistic forecasts, hierarchical reconciliation, intermittent demand and model evaluation.
State Space Models and the Kalman Filter
The Kalman filter is usually introduced as a tracking algorithm for spacecraft. It is more useful understood as the general engine for estimating hidden stat...
Read article
Global vs Local Models in Time Series Forecasting
The traditional approach fits one model per series. Modern practice often fits a single model across thousands of them, and usually wins.
Read article
Anomaly Detection in Time Series
Outlier detection asks whether a value is unusual. Time series anomaly detection asks whether it is unusual *now*, which is a different and harder question.
Read article
Probabilistic Forecasting: Beyond the Point Estimate
A point forecast answers the wrong question. Most decisions depend on how bad things could plausibly get, which is a statement about the whole distribution.
Read article
Missing Data and Irregular Sampling in Time Series
A missing row in a table is a nuisance. A missing interval in a time series changes the meaning of every lag, window and seasonal index computed from it.
Read article
Hierarchical Forecasting: Making Forecasts Add Up
Forecast every store separately and the total will not match the forecast you made for the company. Reconciliation is how you make a hierarchy of forecasts c...
Read article
Feature Engineering for Time Series Without Leaking the Future
Turning a time series into a tabular problem unlocks powerful models and introduces a specific failure: features that quietly contain information from the fu...
Read article
Multiple Seasonality: MSTL, TBATS, and Fourier Terms
Hourly and daily data rarely has one season. Electricity demand cycles daily, weekly and annually at the same time, and a single seasonal period cannot repre...
Read article
Forecasting Baselines That Are Hard to Beat
An RMSE of 4.2 means nothing on its own. Without a baseline you cannot tell whether a model is skilful or merely arithmetic.
Read article
Intermittent Demand Forecasting: Croston's Method and Its Successors
Spare parts and slow-moving stock produce series that are mostly zeros. Standard forecasters quietly fail on them; Croston's method and its successors are bu...
Read article
Forecast Combination: Why Averaging Usually Wins
Choosing the best model is the obvious strategy. Averaging several is usually better, and the reason is not that the average is smarter but that it is less w...
Read article
Regime-Switching Models for Time Series
A single model fitted across a recession and an expansion describes neither. Regime-switching models allow the dynamics themselves to change, with the regime...
Read articleDiagnostics and data quality
Non-stationarity, missing intervals, anomaly detection, feature leakage and seasonal structure.
Week Over Week: A Comparison That Moves Five Percent on Its Own
Today against the same day last week moved 6 percent, so the channel gets investigated. On a metric where nothing has changed at all, that comparison has a s...
Read articleState Space Models and the Kalman Filter
The Kalman filter is usually introduced as a tracking algorithm for spacecraft. It is more useful understood as the general engine for estimating hidden stat...
Read articleGlobal vs Local Models in Time Series Forecasting
The traditional approach fits one model per series. Modern practice often fits a single model across thousands of them, and usually wins.
Read articleAnomaly Detection in Time Series
Outlier detection asks whether a value is unusual. Time series anomaly detection asks whether it is unusual *now*, which is a different and harder question.
Read articleProbabilistic Forecasting: Beyond the Point Estimate
A point forecast answers the wrong question. Most decisions depend on how bad things could plausibly get, which is a statement about the whole distribution.
Read articleMissing Data and Irregular Sampling in Time Series
A missing row in a table is a nuisance. A missing interval in a time series changes the meaning of every lag, window and seasonal index computed from it.
Read articleHierarchical Forecasting: Making Forecasts Add Up
Forecast every store separately and the total will not match the forecast you made for the company. Reconciliation is how you make a hierarchy of forecasts c...
Read articleFeature Engineering for Time Series Without Leaking the Future
Turning a time series into a tabular problem unlocks powerful models and introduces a specific failure: features that quietly contain information from the fu...
Read articleMultiple Seasonality: MSTL, TBATS, and Fourier Terms
Hourly and daily data rarely has one season. Electricity demand cycles daily, weekly and annually at the same time, and a single seasonal period cannot repre...
Read articleForecasting Baselines That Are Hard to Beat
An RMSE of 4.2 means nothing on its own. Without a baseline you cannot tell whether a model is skilful or merely arithmetic.
Read articleIntermittent Demand Forecasting: Croston's Method and Its Successors
Spare parts and slow-moving stock produce series that are mostly zeros. Standard forecasters quietly fail on them; Croston's method and its successors are bu...
Read article
Fourier Analysis for Data Science: From Signals to Features
Fourier analysis is more than a signal-processing trick. It is a way to ask which cycles, rhythms, and scales explain variation in data.
Read article