<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Python on Kwak Jazz</title><link>https://gwils28.github.io/en/tags/python/</link><description>Recent content in Python on Kwak Jazz</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2026 Wilson Goma</copyright><lastBuildDate>Mon, 09 Nov 2026 01:15:19 +0100</lastBuildDate><atom:link href="https://gwils28.github.io/en/tags/python/index.xml" rel="self" type="application/rss+xml"/><item><title>Shelterkwak: tracking a household's crisis preparedness, tier by tier</title><link>https://gwils28.github.io/en/projects/shelterkwak/</link><pubDate>Mon, 09 Nov 2026 00:00:00 +0000</pubDate><guid>https://gwils28.github.io/en/projects/shelterkwak/</guid><description>An app installed on the phone, with no account and no third-party service: 279 goals in nine blocking tiers, a level that drops when supplies expire, an inventory that computes the household&amp;rsquo;s days of self-sufficiency, and an offline Crisis screen for the day itself.</description></item><item><title>Time series foundation models: forecasting tomorrow's electricity consumption for 3,517 commercial buildings</title><link>https://gwils28.github.io/en/projects/flexgrid-prevision-j1/</link><pubDate>Sun, 08 Nov 2026 00:00:00 +0000</pubDate><guid>https://gwils28.github.io/en/projects/flexgrid-prevision-j1/</guid><description>The TSFM tech watch applied to a fictional balance responsible party, on real public data: LightGBM, Chronos-2 and their blend against RTE&amp;rsquo;s day-ahead forecast, published and debiased. A 2.7% MAPE on the portfolio against 5.6% without a model, a router that does not beat the blend, a cold start handled by the naive forecast, Timestral and TimesFM 3.0 in an addendum, a Dagster service and its replayed monitoring.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://gwils28.github.io/projects/flexgrid-prevision-j1/cover.png"/></item><item><title>Time series foundation models: zero-shot against the specialist</title><link>https://gwils28.github.io/en/posts/tsfm-zero-shot-specialiste/</link><pubDate>Fri, 06 Nov 2026 00:00:00 +0000</pubDate><guid>https://gwils28.github.io/en/posts/tsfm-zero-shot-specialiste/</guid><description>When does a foundation model used without any training beat a model fitted on the series? What I observed over a year of French electricity load, with interval coverage and not only the mean error, then rebuilding a router between the two on 50 series.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://gwils28.github.io/posts/tsfm-zero-shot-specialiste/router_features.webp"/></item><item><title>Causal impact on time series: measuring what a grocery chain's promotions really bring in</title><link>https://gwils28.github.io/en/projects/novaretail-incrementalite-promos/</link><pubDate>Thu, 29 Oct 2026 00:00:00 +0000</pubDate><guid>https://gwils28.github.io/en/projects/novaretail-incrementalite-promos/</guid><description>The BSTS counterfactual from the tech watch applied to the promotions of a fictional grocery chain on the Favorita data: campaigns detected rather than assumed, a donor registry, coverage measured on planted truth (0.83 to 0.90 against 0.5 to 0.7 for iid estimators), the earthquake as a test bench, Double ML, a Dagster pipeline and monitoring.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://gwils28.github.io/projects/novaretail-incrementalite-promos/cover.png"/></item><item><title>Claude Code for data science: what changes in my working method</title><link>https://gwils28.github.io/en/posts/claude-code-methode-revisee/</link><pubDate>Thu, 29 Oct 2026 00:00:00 +0000</pubDate><guid>https://gwils28.github.io/en/posts/claude-code-methode-revisee/</guid><description>The lever is no longer the prompt but the context: what goes in, when, and what is handed to mechanisms that do not depend on the model. My method, revised as of 29 October 2026.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://gwils28.github.io/posts/claude-code-methode-revisee/cover.png"/></item><item><title>Causal impact on time series: the counterfactual and its placebos</title><link>https://gwils28.github.io/en/posts/impact-causal-series-temporelles/</link><pubDate>Tue, 27 Oct 2026 00:00:00 +0000</pubDate><guid>https://gwils28.github.io/en/posts/impact-causal-series-temporelles/</guid><description>A counterfactual is a forecast. What I observed building one by hand, measuring its coverage on 100 panels with a known truth, planting four violations of the design, and applying it to Volkswagen&amp;rsquo;s share price after dieselgate.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://gwils28.github.io/posts/impact-causal-series-temporelles/cover.png"/></item><item><title>Retraining frequency in forecasting: accuracy, freshness and stability</title><link>https://gwils28.github.io/en/posts/frequence-reentrainement/</link><pubDate>Sat, 17 Oct 2026 00:00:00 +0000</pubDate><guid>https://gwils28.github.io/en/posts/frequence-reentrainement/</guid><description>Accuracy, freshness and stability don&amp;rsquo;t call for the same retraining frequency. What I observed on a ten-policy bench, an analytical model, the Monash electricity data and a causal simulation.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://gwils28.github.io/posts/frequence-reentrainement/cover.png"/></item><item><title>Hierarchical reconciliation: MinT from scratch, tested on electricity demand in 12 French regions</title><link>https://gwils28.github.io/en/projects/w37-reconciliation-hierarchique/</link><pubDate>Thu, 08 Oct 2026 00:00:00 +0000</pubDate><guid>https://gwils28.github.io/en/projects/w37-reconciliation-hierarchique/</guid><description>MinT rewritten from scratch, its assumptions tested one by one, and an éCO2mix mini-project (12 regions → France) where bottom-up beats the reference method, over 356 origins, two years and two base models.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://gwils28.github.io/projects/w37-reconciliation-hierarchique/cover.png"/></item><item><title>Kwak Finance: a household's budget and net worth, self-hosted</title><link>https://gwils28.github.io/en/projects/kwak-finance/</link><pubDate>Thu, 08 Oct 2026 00:00:00 +0000</pubDate><guid>https://gwils28.github.io/en/projects/kwak-finance/</guid><description>Self-hosted web app for household budgeting and net worth. Keyboard entry, fixed expenses, budget targets, reports, a monthly net-worth matrix, financial goals in a pyramid with their analysis; a Monte Carlo net-worth projection is on its way.</description></item><item><title>Hierarchical forecast reconciliation: promises and limits</title><link>https://gwils28.github.io/en/posts/reconciliation-hierarchique/</link><pubDate>Tue, 06 Oct 2026 00:00:00 +0000</pubDate><guid>https://gwils28.github.io/en/posts/reconciliation-hierarchique/</guid><description>MinT guarantees coherence, not accuracy, and not calibration. Shown through theory, simulation, French grid data and count sales.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://gwils28.github.io/posts/reconciliation-hierarchique/cover.png"/></item><item><title>Claude Code for data science and ML-AI Engineering: my working method</title><link>https://gwils28.github.io/en/posts/claude-code-data-science/</link><pubDate>Tue, 29 Sep 2026 00:00:00 +0000</pubDate><guid>https://gwils28.github.io/en/posts/claude-code-data-science/</guid><description>A fast-coding agent doesn&amp;rsquo;t replace method. How I work with Claude Code: context as the scarce resource, verification as the one non-negotiable step.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://gwils28.github.io/posts/claude-code-data-science/cover.png"/></item></channel></rss>