Using ARIMA to Forecast Your Weekly Dataset
I was reading a Reddit thread in which the OP called for help forecasting some of the weekday performance in the dataset. Machine learning allows you a few ways to do this.
This is the area of time series forecasting. There are two main ways to do this. First, you can use neural networks like LSTMs. Which takes a sequence of data and predicts the next time window. The second is to use the methods from the stats world. Mainly stuff like ARIMA.
In this article, we are just going to focus on using ARIMA. A technique used in the stats world for forecasting.
Because ARIMA is easier to set up and understand compared to a neural network. Also, very useful if you have a small dataset.
One of my projects was to forecast rainfall in a certain area. It did not work well as I hoped. But it will likely work better if you have a clear correlation between variables.
A person in the thread gave a good resource for ARIMA. https://www.askpython.com/python/examples/arima-model-demonstration
Cause I’m not an expert in time series forecasting I can give you some resources you check out.
https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/
https://www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python/
Some general tasks you want to do:
- Make sure your data is stationary
- Install pmaria
- If your data is seasonal use SARIMA instead.
After using the resources above, you then forecast the win-loss ratio for your dataset or any other variable you want to forecast in the future.
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The Two Kinds of Writers, The One I Am Right Now, And The One I’m Aspiring To Be
Recently I read a decent article by Darius Foroux called Arthur Schopenhauer: There Are Only Two Kinds Of Writers. Which talked about the Arthur Schopenhauer idea of two writers.
There are above all two kinds of writers: those who write for the sake of what they have to say and those who write for the sake of writing. The former have had ideas or experiences which seem to them worth communicating; the latter need money and that is why they write — for money.
If you hang out on Twitter or medium often, you probably notice more of the second writer. Publish a lot but seems to be a rehash of what somebody else says earlier. In some areas of twitter, it has only devolved into many buzzwords strung together as a tweet. Not meaning anything if you take 5 seconds to think about it.
This is not to say I'm not guilty. I tend to publish on a regular basis so many of the articles I have published frankly may not be my best work. But I do try to write about an interesting idea I found out or something that I learnt.
Build a business, not an audience. Touches on the same idea. That people should do interesting stuff with their lives then write about. Rather than “remixing” what other people have done. Doing interesting stuff leads to interesting content. It’s a pretty simple equation.
But to continue on the content treadmill. You have cut corners. But this leads to less than stellar content. As mentioned earlier bit twitter can descend into buzzwords.
We forget but one of the main goals of creating content is delivering value. Just tweeting for the sake of tweeting brings value to no one.
An insightful tweet from a unique observation of the world is very helpful for everybody. As Schopenhauer says former have had ideas or experiences which seem to them worth communicating.
Writing for money and writing to say something interesting are not separate activities. Lots of people can be both pretty well. But it can be easy to fall into the writing for money. In this case, writing for more followers or prestige.
If your content does help your reader, why are your writing in the first place?
Maybe you tweet or blog to use it as a journal. That’s great. As explaining how you area dealing with the problem at hand. Shows your thought process. And maybe useful for people in a similar situation.
This is different from manufacturing platitudes.
What is an audience anyway?
I think Alex Hillman (via farez) has the best definition:
https://farez.me/audience-building-for-saas/
"An audience is a group of people with common goals and interests, that you can study and most importantly SERVE". People in your audience look forward to learning from you, and to engage with you, and for you to engage with them too. Building an audience can benefit you as an individual and benefit your SaaS business.
What we confuse an audience with is followership:
Followership: This is when you're purely focused on the number of people who are following you. If your goal is to have more Twitter followers today than you had yesterday, then you're building followership, not an audience. If you intend to build a business with and learn from those who follow you, then building followership is the least effective way.
Who are we serving?
This question can help us see the forest for the trees.
Like the journaling section above you don’t need to consciously think about serving your audience. If you are writing about how you made your latest project and what you learnt. The reader will find something very useful from the blog post. As you likely take about your unique angle for tacking the project and produced some new ideas after finishing the project.
Why I’m writing this?
Likely as a reminder for myself. To make sure every blog post I create has some type of value for the reader. To make sure I don’t write about useless feel-goods on Twitter or elsewhere for that matter.
Finding A Third Way For Remote Work
Recently I read a great article by Cal Newport putting a new spin on remote working. He calls for people to make use of third spaces for our work. Rather than working from home.
Taking your laptop to a rented office or a Starbucks is not a new idea. But Cal Newport argues that employers should subsidize their worker’s offices. Due to the increased productivity gains, he argues it’s a no brainer. As investment will pay for itself.
At the beginning of the article, Cal Newport introduces the scene of many authors who rented space to do their work. Maya Angelou hired a bare hotel when she wanted to write. John Steinbeck went on a fishing boat to do his writing. JK Rowling was famous for renting hotel space in Scotland.
Cal Newport mentioned that writers may have been the first work from home knowledge workers. After the pandemic, many people may have to follow their lead as we continue remote working.
One of the issues from working from home is the lack of separation between home life and work life. As you are completing your presentation next to your laundry basket. Your brain is half occupied thinking about the various house duties that you need to do. Eating away at your concentration.
Cal notes:
Because the laundry basket is embedded in a thick, stress-inducing matrix of under-attended household tasks, it creates what the neuroscientist Daniel Levitin describes as “a traffic jam of neural nodes trying to get through to consciousness.” Angelou, by shifting her work to a hotel room with bare walls, was cultivating an effective mental space to compose poetry by calming her relational-memory system.
This is why many people recommend, you still keep the routines you had when heading to the office. So your mind creates the mental shift getting into work mode.
This is why cal mentions:
Many workers won’t be returning to an office anytime soon, but having them relocate their efforts entirely to their homes for the long run might be unexpectedly misery-inducing and unproductive. We need to consider a third option for our current moment, and if we look to authors for inspiration then one such alternative emerges: work from near home.
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A co-working space, a small office above a Main Street store, a rented garage apartment, or even a spruced-up shed can enable a much more satisfying and effective experience tackling cognitive work than the laptop on the kitchen table, or the home-office desk in the bedroom.
This model of remote work allows us to take advantage of remote work. Without dealing with the mental fatigue challenge of dealing with our home environment.
You would make a good argument saying this is a promotion for co-working spaces and coffee shops. You won’t be wrong. But the article allows us to think about how we can improve remote work even more.
In the article, he mentioned a British startup called Flown. An Airbnb for office space. You can rent a room and desk with an amazing view of the Cotswolds or rent a house near a Portuguese beach.
I think this can be the upgrade of the nomad lifestyle that was popular a few years ago. As the short space away from your normal environment allows you time to think. But not too long that you are worried about getting a new visa. The short-stay allows you to get to your friends and family in a short time.
Flown allows for bigger spaces, to invite co-workers and other people to work on your project.
The startup is said to be targeting companies that want to buy in bulk. So employees can work in a new location that can help creative output.
On the website they described team off-sites:
Team off-sites are opportunities to bring a team together to connect and collaborate in person. For teams usually co-located, it’s a chance to get away from the day-to-day for a creative boost. For teams usually remote, it’s a chance to form valuable real-world bonds.
As I mentioned earlier with the return on investment, Cal Newport same something similar:
If an organization plans to allow remote work, the extra cost to subsidize the ability of workers to escape household distraction will be more than recouped in both the increased quality of work produced and the improved happiness of the employees, leading to less burnout and reduced churn. Strictly from the perspective of dollars and cents, W.F.N.H. is likely a superior policy to W.F.H. It’s an up-front investment that promises strong returns in the long run.
Remote work could get even better with the development of better technology. Notably, Starlink may allow for fast internet speeds in the most rural areas. If Starlink works then you could read your presentation in the middle of the sea or check up on your email during a long mountain hike.
Thanks to covid lots of people have moved into suburbia. So if you have a large acre of land. You could work directly in a field enjoying nature at the same time.
Work near home help with more minor issues. Like lack of space at home. But one of the main barriers to Work near home is cost. Not everybody can afford to work in a third space. Hence the importance of subsidizing worker’s offices
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Some YouTube channels that review papers
When I was reading a Reddit thread. People were wondering if there were YouTubers reviewing papers. As the OP noticed that one of the YouTuber's that he regularly watched stopped uploading videos. There are a few YouTubers that talk about ML and review papers.
I decided to compile some of the YouTube channels into this short list.
Two Minute Papers does great overviews of fascinating papers. Showing the increasing progress of ML.
Some of the videos I liked:
- 4 Experiments Where the AI Outsmarted Its Creators
This video showed various AI solving a problem not in the way the researchers intended to. That may include abusing the physics in the simulation or lateral thinking used by the model.
- A Video Game That Looks Like Reality!
A review of a paper that takes GTA V gameplay and converts them to photo-realistic footage.
Yannic Kilcher does in-depth reviews of various papers. As you go through the paper he shows you his thought process. And showing what important inside the paper. Very useful if don’t read that many papers. (Like me)
Some good videos:
A review of a paper that introduced transformers.
- DeepMind's AlphaFold 2 Explained! AI Breakthrough in Protein Folding What we know (& what we don't)
A great rundown on protein folding and speculating how Alphafold 2 works.
- GPT-3: Language Models are Few-Shot Learners (Paper Explained)
A comprehensive paper reading of the GPT-3 paper.
Bycloud you may have seen him around on Reddit. Creates short and insightful summaries of papers.
- AI Sky Replacement with SkyAR
Summary of paper that creates AR effects in video footage. Adding various effects to the video footage’s sky.
- AI Generates Cartoon Characters In Real Life [Pixel2Style2Pixel]
Reviewing a paper that converts cartoon characters to real-life equivalents and vice versa. Also explains how the paper made it easier to adjust the parameters of the GAN. Helping us adjust what images we want to produce.
This is a podcast series that interviews top ML researchers. While they don’t have videos about papers alone. As they interview various experts in the field. So they talk about many papers as a consequence.
While this is a short list maybe you can find these channels interesting and learn something new.
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Some Maths Resources to Help You in Your ML Journey
I have been looking for content to improve my maths skills for ML. I have also noticed when scrolling a few threads many people did not find content that explains maths in an intuitive manner. Leading to a lack of belief in learning ML. But this does not have to be.
I’m with you, odd-looking characters and Greek letters don’t look welcoming. But they are some good teachers online that can demystify that experience.
Some of those materials are below:
3blue1brown Calculus and Linear Algebra series
I remember watching both of these series a while. And I will be watching them again. The narrator explores the topic without getting bogged down in the details. Feels like your discovering the maths with the original people who made calculus. In the linear algebra series, he does such a great job visualising vector space. You can see the various operations done to vectors and matrices in picture form.
3blue1brown Deep Learning series
Taking the concepts from the previous series and applying them to deep learning.
I’m sure you know about Sal Kahn by now. As you watched a couple of his videos. His video intuitively explains various topics. Also, show you the various hand by hand actions you need to take to do various calculations. Like matrix multiplication and calculating derivatives.
Mathematics for Machine Learning book
I tend to use this book as a reference guide if it’s a concept I want to check out. This book goes through the most important subjects relevant to machine learning and goes in-depth.
Mathematics for Machine Learning - Multivariate Calculus – Imperial College London
A multi-hour series explaining how calculus is used in deep learning. The material comes at the subject with a high-level view. But goes into sufficient enough detail to help you learn a lot.
Understand Calculus in 35 Minutes - The Organic Chemistry Tutor
A general overview of the subject. So you can be familiar with the concepts for deep learning later on.
NOTE: you won’t learn all of calculus in 30 minutes. But the video will help you get accustomed to the main ideas of the subject.
Now, these are resources that I have not used or have used very lightly but gotten good recommendations from various people.
So check them out:
This course talks about the linear algebra used in real computation. Not just Linear algebra done by hand.
Deep Learning book by Ian Goodfellow and Yoshua Bengio and Aaron Courville
From their website:
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.
I have not thoroughly read all of the book. But I have used the notation page to understand maths symbols in various deep learning work.
An Introduction to Statistical Learning
A few people in this subreddit and the main subreddit have recommended this book. But I have never read it.
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We can’t comprehend how big the world is
I was reading an article about scientific illiteracy. Which author was recounting a story that he was teaching his class a physics problem which you need to estimate the population of the USA. But worked a significant amount of students underestimated the US population. Some overestimated it by a billion. He said that the students were “innumerate”. As they did not grasp what a million or billion was.
I found a similar concept in an article called The Economy is Mind-Bogglingly Huge. In the article, he explains that tons of industries you have never heard about that keeps the world spinning.
In the article he gave this example:
I was talking to the owner of a scale (to weigh things) business in the Midwest. People ask him "Is that a full-time job? Is that industry big enough to support you?"
His response: "Look around this room. Everything you own, everything you see, and everything you eat has been weighed. Multiple times."
When a product (let’s say flour) is farmed and processed, it is weighed. When it is packed into shipping containers, those are weighed. When they are unloaded from the ship, they are weighed again. When the truck is loaded with pallets it is weighed again. A customer buying flour may weigh it again while they’re making their recipe, and then weigh themselves after eating their cookies.
Then added:
Have you ever thought about how many times your flour is weighed? How many scales are built, sold, repaired, and serviced in the supply chain of just your flour?
Granted this does not take into account the decreasing globalisation and localising of supply chains. But it gets the point across. Supply chains as we can see in the example above can still involve numerous parties in just one area. Never mind a whole country or the entire world.
Software Economy is Bigger Than You Think
In the software world, a similar observation was made called the Patio11 Law. Patio11’s Law states the software economy is bigger than you think, even when you take into account Patio11’s Law.
Patio11 (Patrick Mckenzie) gave an example in a podcast. About people making decent cash making software for kitchen countertop installers. Kitchen remodelling can cost a lot of money if you a going for a high end remodel. There is a large field of local companies doing these. And they have serious questions they want answers to. Like how much marble do I buy from the store? Because if you don’t buy enough you will work out halfway through that you have an incomplete kitchen. Also if you buy too much then work out that you spend too much money on the material. Having software can give you the answers to the question. This allows the software maker to create a boatload of money. With a field, you never heard about.
He also mentions there is software for cemetery management. Software that can help lay out cemeteries and other functions. While this is morbid. It does help illustrate the point software and people creating companies is everywhere you see.
In this blog post. The person gave more examples.
Austen Allred shared how, when matching Lambda graduates to jobs, he’ll discover software companies he’s never heard of in Oklahoma pocketing $10m/year in profit. Doing things like “making actuarial software for funeral homes.”
My favorite example is ConvertKit. None of my friends have heard of ConvertKit. They ended 2019 with $20 million in ARR. Revenue is growing 30% year-over-year. They have 48 employees.
To be fair Nathan Barry is pretty well known in the bootstrapper scene. But compared to the YC companies it is a drop in the bucket.
In the article he also mentioned that:
Of the 3,000+ software companies acquired over the last three years, only 7% got TechCrunch, Recode, HN, or other mainstream tech coverage.
So they are thousands of software companies hanging in the background, making a load of cash. Also, I think that these companies are not invisible. There are invisible to us. That funeral home software I’m sure has some presence in the industry. By word of mouth or marketing via B2B means.
Think of AWS if you’re a developer or if you’re somewhat aware of tech. You probably know that AWS is one of the most important companies around. But if you ask your grandfather about it. He will give you an odd look.
It’s the reason why we call many of these areas niches. Only a select few people care about the subject. But that is more than enough to make a lot of money.
The internet has allowed us to connect to billions of people are around the world with no geographic limit. So this allows us to fulfil niches that we could not have before due to geography. There are millions of niches one could get involved in. Some niches are more valuable than others. The media only writes articles about a select few of those niches.
See: Ben Thompson never-ending-niches
So it would make sense that you could create millions of dollars and nobody has heard about you. Because there is so much to do with billions of people on the planet. Billions of people in the world mean billions of people to serve.
This does not mean they are not roadblocks for opportunity. Racism, sexism, corruption etc. But the pie is getting bigger and we can be part of it.
Massive Supply Chains Around the World
Right now, semiconductors are in the news because we have a shortage of them. And many companies can’t create new products. Car companies can’t make new cars. Tech companies can’t make new laptops and phones. Sony can’t make new PlayStations.
The semiconductor industry has hundreds of players. Some very big, some very small. You have some companies that only make the blueprints. You have some companies that only make the chips. You have companies that only make the software for the chip. Then you have companies that add the chip they add to their device. And there is way more I’m missing.
When making a chip, they are companies that make equipment for those chip manufacturers. The most famous and important example is the Dutch company ASM. That makes million-dollar machines to cut chips using lasers. (I'm not kidding). This Dutch company is the only company in the world that can do lithography to such a high level.
Check out this video by TechAltar explaining the semiconductor industry.
Some of the famous covid vaccines take at least 200 components to manufacture. You can bet that there are companies where their only job is to make some of those materials.
Bits of the internet that was forever free are now becoming monetised. Patron and Onlyfans are the latest examples. But twitter announced upcoming monetization features for creators. Spotify and Apple are looking to have exclusive podcast episodes. So many people can serve their niches while getting paid. Most of these creators will not know about it. But enough people will be enough to sustain them. Think of Kevin Kelly 1,000 true fans.
Blockchain and decentralised finance promise to do even more. But has yet to hit mainstream adoption outside of speculation.
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