Proper algorithm for short term predictions - machine-learning

I am currently working on my final project in university and I have to do some Machine Learning. I have to say I am not experienced with ML. I have data with a timestamp, zone number (6 zones) and number of calls. I need to predict the number of calls and initially i decided to use Multilinear regression. However, while researching i found about time series analysis and I am wondering now, which one would be better for making predicitons in my case.
From what I understood time series analysis is good for forecast, but is it good for short term predictions. Like predicting number of calls tomorrow or next week? I want to make short term predictions. Maximum in the next month.
I just have read so much that I got confused!
I would very much appreciate, if you could advice me, what is better.

Related

What model to use for forecasting when customers will arrive?

I'm trying to build a model which will give the probability of every customer in a database will show up on a certain day (i.e. I pass in 8/25/19 and the list of all customers shows up with their respective probability). I have the logs for all customers transactions and the date. I'm thinking of using some sort of RNN to do this. Is this the proper way to do this? If not, what is the best way to do it? I want to discover the patterns and high confidence leads for which customers show up. There is around 400,000 records for 3 years.
You have time series data.
RNN is a good starting point. Check out this step-by-step instructions of sales prediction. RNN is an easy start and might give you really good quality. Also there is an adaptation of xgboost algorithm for time series that also gives a good quality, but might be slower.
Good luck!

How to predict the time of occurrence of specific event on daily basis using machine learning?

I am collecting the activities of a person in a day with time stamp data .Assume I am tracking 4 different activities that person is doing and an event occurring time in that day.event can occur multiple times also in a day. I am trying to predict the event occurring time in a day using the historical data to train a model.
My model should give an out put as a time having the maximum probability of that event to happen.
please suggest what should be the machine learning approach to this problem.
Thanks in advance for the help on this.
If you have a background in machine learning/statistics then You going to achieve your project within minimal time.
Here is a glimpse for you.
Machine learning has various algorithms to implement, depending on the kind of the problem you're solving.so,in this case your problem can be solved by predictive analysis model which is based on predicting time driven events effectively.And under the hood,you'd apply nor use regression algorithm.(linear/logistic)
It uses historical data to predict future events and still the historical data can be used to generate mathematical model which you can as well use to capture important events or trends.Then for predictive model,you can use it on current data to predict what time an event will happen.
For your information,there are software packages/libraries which can as well help you to implement the above algorithm effectively.
Hope this helps.

What kind of classifier is used in the following scenario?

If I am building a weather predictor that will predict if it is will snow tomorrow, it is very easy to just straight away answer by saying "NO".
Obviously, if you evaluate such a classifier on every day of the year, it would be correct with an accuracy at 95% (considering that I build it and test it in a region where it snows very rarely).
Of course, that is such a stupid classifier even if it has an accuracy of 95% because it is obviously more important to predict if it will snow during the winter months (Jan & Feb) as opposed to any other months.
So, if I have a lot of features that I collect about the previous day to predict if it will snow the next day or not, considering that there will be a feature that says which month/week of the year it is, how can I weigh this particular feature and design the classifier to solve this practical problem?
Of course, that is such a stupid classifier even if it has an accuracy of 95% because it is obviously more important to predict if it will snow during the winter months (Jan & Feb) as opposed to any other months.
Accuracy might not be the best measurement to use in your case. Consider using precision, recall and F1 score.
how can I weigh this particular feature and design the classifier to solve this practical problem?
I don't think you should weight any particular feature in any way. You should let your algorithm do that and use cross validation to decide on the best parameters for your model, in order to also avoid overfitting.
If you say jan and feb are the most important months, consider only applying your model for those two months. If that's not possible, look into giving different weights to your classes (going to rain / not going to rain), based on their number. This question discusses that issue - the concept should be understandable regardless of your language of choice.

Using a feature as Input vs. using it to build Several Machines on SVM

I am an undergraduate student and for my graduation thesis I am using SVM to predict the arrival time of a bus to a bus stop in its route. After doing a lot of research and reading some papers I still have a key doubt about how to model my system.
We've decided which features to use and we are in the process of gathering the data required to perform the regression, but what is confusing us are the implications or consequences of using some features as input for the SVM or building separated machines based on some of these features.
For instance, in this paper the authors built 4 SVMs for predicting bus arrival times: one for rush hour on sunny days, rush hour on rainy days, off-rush hour on sunny days and the last one for off-rush hours and rainy days.
But on a following paper on the same subejct they decided to use a single SVM with the weather condition and the rush/off-rush hour as input instead of breaking it in 4 SVMs as before.
I feel like this is the kind of thing that is more about experience so I would like to hear from you guys if anyone has any information about when to choose one of these approaches.
Thanks in advance.
There is no other way: you have to find out on your own. This is why you have to write this thesis. Nobody starts with a perfect solution. Everyone makes mistakes. Your problem is not easy and you cannot say what will work when you have never done anything similar. Try everything you found in the literature, compare the results, develop your own ideas, ...
Most important question: what is the data like?
Second question: what model do you expect to capture this?
So if you want to use SVMs for some reason, keep in mind their basic mechanism is linear, and can only capture non-linear phenomena if data is transformed by a suitable kernel.
For a particular problem at hand that means:
Do you have reason (plots, insights in the problem nature) to believe your problem is linear(ly separable)? Just use one linear svm.
Do you have reason your problem consist of several linear subproblems? Use a linear svm on each of the subproblems.
Does your data seem non-linearly grouped? Try an svm with something like rbf kernel.
Of course, you can just plug in and try, but checking the above may increase understanding of the problem.
In your particular problem I would go for single SVM.
With my not so extensive experience, I would consider breaking a problem in several SVMs for following reasons:
1)The classes are too different, or there are classes and subclasses in your problem.
E.g. in my case: there are several types of antibodies in a microscope image and they all may be positive or negative. So instead of defining A_Pos, A_Neg, B_Pos, B_Neg, ... I decide first if the image is positive or negative and determine the type in second SVM.
2)The feature extraction is too expensive. Provided you have groups of classes, which may be identified with fever features. Instead of extracting all features for a single machine, you may first extract only a small subset, and if required (result not with high enough probability) extract further features.
3)Decide whether the instance belongs to problem at all. Make a model containing one class and all instances of training set. If the instance to be classified is an outlier, stop. Otherwise classify with 2nd SVM containing all classes.
The key-word is "cascaded SVM"

Using Artificial Intelligence (AI) to predict Stock Prices

Given a set of data very similar to the Motley Fool CAPS system, where individual users enter BUY and SELL recommendations on various equities. What I would like to do is show each recommendation and I guess some how rate (1-5) as to whether it was good predictor<5> (ie. correlation coefficient = 1) of the future stock price (or eps or whatever) or a horrible predictor (ie. correlation coefficient = -1) or somewhere in between.
Each recommendation is tagged to a particular user, so that can be tracked over time. I can also track market direction (bullish / bearish) based off of something like sp500 price. The components I think that would make sense in the model would be:
user
direction (long/short)
market direction
sector of stock
The thought is that some users are better in bull markets than bear (and vice versa), and some are better at shorts than longs- and then a combination the above. I can automatically tag the market direction and sector (based off the market at the time and the equity being recommended).
The thought is that I could present a series of screens and allow me to rank each individual recommendation by displaying available data absolute, market and sector out performance for a specific time period out. I would follow a detailed list for ranking the stocks so that the ranking is as objective as possible. My assumption is that a single user is right no more than 57% of the time - but who knows.
I could load the system and say "Lets rank the recommendation as a predictor of stock value 90 days forward"; and that would represent a very explicit set of rankings.
NOW here is the crux - I want to create some sort of machine learning algorithm that can identify patterns over a series of time so that as recommendations stream into the application we maintain a ranking of that stock (ie. similar to correlation coefficient) as to the likelihood of that recommendation (in addition to the past series of recommendations ) will affect the price.
Now here is the super crux. I have never taken an AI class / read an AI book / never mind specific to machine learning. So I cam looking for guidance - sample or description of a similar system I could adapt. Place to look for info or any general help. Or even push me in the right direction to get started...
My hope is to implement this with F# and be able to impress my friends with a new skill set in F# with an implementation of machine learning and potentially something (application / source) I can include in a tech portfolio or blog space;
Thank you for any advice in advance.
I have an MBA, and teach data mining at a top grad school.
The term project this year was to predict stock price movements automatically from news reports. One team had 70% accuracy, on a reasonably small sample, which ain't bad.
Regarding your question, a lot of companies have made a lot of money on pair trading (find a pair of assets that normally correlate, and buy/sell pair when they diverge). See the writings of Ed Thorpe, of Beat the Dealer. He's accessible and kinda funny, if not curmudgeonly. He ran a good hedge fund for a long time.
There is probably some room in using data mining to predict companies that will default (be unable to make debt payments) and shorting† them, and use the proceeds to buy shares in companies less likely to default. Look into survival analysis. Search Google Scholar for "predict distress" etc in finance journals.
Also, predicting companies that will lose value after an IPO (and shorting them. edit: Facebook!). There are known biases, in academic literature, that can be exploited.
Also, look into capital structure arbitrage. This is when the value of the stocks in a company suggest one valuation, but the value of the bonds or options suggest another value. Buy the cheap asset, short the expensive one.
Techniques include survival analysis, sequence analysis (Hidden Markov Models, Conditional Random Fields, Sequential Association Rules), and classification/regression.
And for the love of God, please read Fooled By Randomness by Taleb.
† shorting a stock usually involves calling your broker (that you have a good relationship with) and borrowing some shares of a company. Then you sell them to some poor bastard. Wait a while, hopefully the price has gone down, you buy some more of the shares and give them back to your broker.
My Advice to You:
There are several Machine Learning/Artificial Intelligence (ML/AI) branches out there:
http://www-formal.stanford.edu/jmc/whatisai/node2.html
I have only tried genetic programming, but in the "learning from experience" branch you will find neural nets. GP/GA and neural nets seem to be the most commonly explored methodologies for the purpose of stock market predictions, but if you do some data mining on Predict Wall Street, you might be able to utilize a Naive Bayes classifier to do what you're interested in doing.
Spend some time learning about the various ML/AI techniques, get a small data set and try to implement some of those algorithms. Each one will have its strengths and weaknesses, so I would recommend that you try to combine them using Naive Bays classifier (or something similar).
My Experience:
I'm working on the problem for my Masters Thesis so I'll pitch my results using Genetic Programming: www.twitter.com/darwins_finches
I started live trading with real money in 09/09/09.. yes, it was a magical day! I post the GP's predictions before the market opens (i.e. the timestamps on twitter) and I also place the orders before the market opens. The profit for this period has been around 25%, we've consistently beat the Buy & Hold strategy and we're also outperforming the S&P 500 with stocks that are under-performing it.
Some Resources:
Here are some resources that you might want to look into:
Max Dama's blog: http://www.maxdama.com/search/label/Artificial%20Intelligence
My blog: http://mlai-lirik.blogspot.com/
AI Stock Market Forum: http://www.ai-stockmarketforum.com/
Weka is a data mining tool with a collection of ML/AI algorithms: http://www.cs.waikato.ac.nz/ml/weka/
The Chatter:
The general consensus amongst "financial people" is that Artificial Intelligence is a voodoo science, you can't make a computer predict stock prices and you're sure to loose your money if you try doing it. None-the-less, the same people will tell you that just about the only way to make money on the stock market is to build and improve on your own trading strategy and follow it closely.
The idea of AI algorithms is not to build Chip and let him trade for you, but to automate the process of creating strategies.
Fun Facts:
RE: monkeys can pick better than most experts
Apparently rats are pretty good too!
I understand monkeys can pick better than most experts, so why not an AI? Just make it random and call it an "advanced simian Mersenne twister AI" or something.
Much more money is made by the sellers of "money-making" systems then by the users of those systems.
Instead of trying to predict the performance of companies over which you have no control, form a company yourself and fill some need by offering a product or service (yes, your product might be a stock-predicting program, but something a little less theoretical is probably a better idea). Work hard, and your company's own value will rise much quicker than any gambling you'd do on stocks. You'll also have plenty of opportunities to apply programming skills to the myriad of internal requirements your own company will have.
If you want to go down this long, dark, lonesome road of trying to pick stocks you may want to look into data mining techniques using advanced data mining software such as SPSS or SAS or one of the dozen others.
You'll probably want to use a combination or technical indicators and fundamental data. The data will more than likely be highly correlated so a feature reduction technique such as PCA will be needed to reduce the number of features.
Also keep in mind your data will constantly have to be updated, trimmed, shuffled around because market conditions will constantly be changing.
I've done research with this for a grad level class and basically I was somewhat successful at picking whether a stock would go up or down the next day but the number of stocks in my data set was fairly small (200) and it was over a very short time frame with consistent market conditions.
What I'm trying to say is what you want to code has been done in very advanced ways in software that already exists. You should be able to input your data into one of these programs and using either regression, or decision trees or clustering be able to do what you want to do.
I have been thinking of this for a few months.
I am thinking about Random Matrix Theory/Wigner's distribution.
I am also thinking of Kohonen self-learning maps.
These comments on speculation and past performance apply to you as well.
I recently completed my masters thesis on deep learning and stock price forecasting. Basically, the current approach seems to be LSTM and other deep learning models. There are also 10-12 technical indicators (TIs) based on moving average that have been shown to be highly predictive for stock prices, especially indexes such as SP500, NASDAQ, DJI, etc. In fact, there are libraries such as pandas_ta for computing various TIs.
I represent a group of academics that are trying to predict stocks in a general form that can also be applied to anything, even the rating of content.
Our algorithm, which we describe as truth seeking, works as follows.
Basically each participant has their own credence rating. This means that the higher your credence or credibility, then the more their vote counts. Credence is worked out by how close to the weighted credence each vote is. It's like you get a better credence value the closer you get to the average vote that has already been adjusted for credence.
For example, let's say that everyone is predicting that a stock's value will be at value X in 30 day's time (a future's option). People who predict on the average get a better credence. The key here is that the individual doesn't know what the average is, only the system. The system is tweaked further by weighting the guesses so that the target spot that generates the best credence is those votes that are already endowed with more credence. So the smartest people (historically accurate) project the sweet spot that will be used for further defining who gets more credence.
The system can be improved too to adjust over time. For example, when you find out the actual value, those people who guessed it can be rewarded with a higher credence. In cases where you can't know the future outcome, you can still account if the average weighted credence changes in the future. People can be rewarded even more if they spotted the trend early. The point is we don't need to even know the outcome in the future, just the fact that the weighted rating changed in the future is enough to reward people who betted early on the sweet spot.
Such a system can be used to rate anything from stock prices, currency exchange rates or even content itself.
One such implementation asks people to vote with two parameters. One is their actual vote and the other is an assurity percentage, which basically means how much a particular participant is assured or confident of their vote. In this way, a person with a high credence does not need to risk downgrading their credence when they are not sure of their bet, but at the same time, the bet can be incorporated, it just won't sway the sweet spot as much if a low assurity is used. In the same vein, if the guess is directly on the sweet spot, with a low assurity, they won't gain the benefits as they would have if they had used a high assurity.

Resources