Valeriy M., PhD, MBA, CQF

🚨 IT’S HAPPENING! 🚨 My book Mastering Modern Time Series Forecasting_ is now up for preorder at a low launch price**! 🔮📉📈

@kevinjohn7286 valeman.gumroad.com math section has link

@predict_addict Hi Valeriy, i am interested in this work I am from Canada.. how can I buy Arithmetic?

LaTaM friends — a special release is coming soon, and I’m looking for a few reviewers with real accounts who understand math. DM me or comment below. Peru 🇵🇪 especially welcome — step up, make your country proud, and earn an honorable mention in the book. #math

@flautas1212 Very soon

@predict_addict When is the release expected?

@Aquelquerecibe Arithmetic

@predict_addict what's one the main book's topic in this case, Valery? I am starting with my review abouth math to become Data Analyst in the future. I have 10 years without math studies, i do not know if this book could be useful for me goal.

@cfcosta_ @stats_feed @grok Food for thought

@predict_addict @stats_feed @grok I mean, if the sad people die the average of happiness goes up

🇫🇮 Finland is the happiest country in the world for the 9th time!

@grok @stats_feed Grok arrives when it is not needed. Try to respond when asked next time

@predict_addict @stats_feed The stats align with WHO/World Bank 2021 data (crude rates per 100k): Finland 14.6, Sweden 13.8, Norway 13.2, Iceland 11.9, Denmark 10.5, global avg ~9.2. Finland tops Nordics but trails USA (15.6) and Japan (~14.7 in some reports). Happiness indexes measure different factors.

@grok refused, so here are the actual numbers (WHO/World Bank, ~2021, per 100k): 🇫🇮 Finland: ~14.6 — above global (~9) and EU averages Nordics: 🇸🇪 Sweden ~13.8 🇳🇴 Norway ~13.2 🇮🇸 Iceland ~11.9 🇩🇰 Denmark ~10.5 ➡️ Finland is highest among Nordics Europe (selected): 🇬🇧 UK ~9.6 🇪🇸 Spain ~8.7 🇩🇪 Germany ~12.9 🇦🇹 Austria ~14.5 ➡️ Finland is above most Western Europe North America: 🇺🇸 USA ~15.6 (higher) 🇨🇦 Canada ~9.4 (lower) South America: 🇧🇷 Brazil ~7.6 🇦🇷 Argentina ~7.9 ➡️ Finland is ~2x higher Asia: 🇯🇵 Japan ~17.4 (higher) 🇮🇳 India ~12.6 (lower) 🇨🇳 China ~9.0 (lower) Bottom line: Finland is not the worst globally, but it is consistently above average, including vs peers in Europe and the Nordics.

@stats_feed @grok what the per capita number of suicides in Finland compare to other countries in Scandinavia, Europe, north and South America and Asia

Russian math is on another level. In a rural classroom, barefoot peasant children were solving—mentally—problems that would slow down today’s MIT PhDs and quant researchers.

@babakhowa @nnzt4dxvrv You can ask Peru 🇵🇪

@predict_addict @nnzt4dxvrv How to start a soviet style math culture, I am from a small frontier state of India.

Elite math student now came frome Kazakhstan; my dept is full of martian came from kaz.

@ariemannsurface Now you can

@predict_addict Hi Valeriy, can't DM you, but I'm interested in reviewing it

@SamRedlich Good one

@predict_addict clifford algebra is older than clifford 🤓

Solid mathematical ideas almost always outperform contrived engineering tricks. For years deep learning has been dominated by increasingly complex architectural hacks: CNN blocks, attention layers, channel mixers, residual pathways, normalization stacks. Every few years a new architecture is announced as if it were a revolution. One of the most famous examples was Kaiming He and Residual Networks (ResNet). At the time he was paraded around the AI world like a celebrity because residual connections supposedly “solved” deep learning. But these were largely engineering patches. Now something much more interesting appeared. A new architecture called CliffordNet returns to mathematics — specifically Clifford Algebra, developed in the 19th century by William Kingdon Clifford. Instead of stacking arbitrary modules, the model is built around the geometric product uv = u·v + u∧v A single algebraic operation that simultaneously captures inner product structure and geometric interactions. In other words: the math already contains the interaction mechanism. No attention blocks. No mixer layers. No architectural spaghetti. The result: • 77.82% accuracy on CIFAR-100 with only 1.4M parameters • roughly 8× fewer parameters than ResNet-18 And with strict O(N) complexity. The paper even suggests that once geometric interactions are modeled correctly, feed-forward networks become largely redundant. A good reminder for the AI community. Engineering tricks can dominate for years. But eventually mathematics shows up and deletes half the architecture. Paper: [arxiv.org/pdf/2601.06793…) 19th century geometry just walked into computer vision.

@Korvalyov 👍

@predict_addict Hola Valeriy. Me gustaría poder revisar el texto :)

Transformers Are Not “Just Bigger LSTMs” Most people still think Transformers are oversized LSTMs with attention glued on top. They’re not. Recurrent models compress history into a hidden state. Transformers keep the entire history accessible. That architectural decision changes: • Long-range dependency handling • Parallelization • Memory bottlenecks • Forecast horizon stability If you don’t understand why attention changes forecasting dynamics, you’re just copying architectures from NLP. Full breakdown in the new Transformers chapter: Core: valeman.gumroad.com/l/MasteringMod… Pro (extended technical depth + extras): valeman.gumroad.com/l/MasteringMod… #timeseries #forecasting

@Guillermo_02 You are in

@predict_addict Hi, I can't able to send you a DM but I really really want that book :(

@thgmansur Please dm me, Brazilian Portuguese coming next

@predict_addict Hi, I would be glad to help with Brazilian Portuguese when the time comes. Congrats for the initiative.

@alexant1703 👍

@predict_addict Hi, Valeriy. I wrote you a DM.

They are now finally available in English. Kiselev Algebra (Part I) valeman.gumroad.com/l/kiselev_alge… Kiselev Arithmetic (Amazon hardcover & paperback) a.co/d/0g6I2PaM?utm… Study mathematics. Teach your kids mathematics.

It’s a small pedagogical detail, but it reveals a deeper philosophy: good textbooks try to reflect the structure of mathematics, not wrap it in memorisation tricks. Kiselev’s books educated generations of mathematicians across the Russian Empire and the USSR for more than a century.

Small details in mathematics education often reveal how entire systems think. Take something as basic as the order of operations. In the United States and the U.K., children are typically taught the rule using the acronym PEMDAS: Parentheses Exponents Multiplication Division Addition