There are many models available online, but the question is, which one s do you need to get to reach your goal? After testing multiple complex and less complex models, the ARIMA model was best suited for our purpose. This relatively simple model does not need a lot of data or variables take weather as an example to make a prediction, making it more practical.
The model you use should always strike a balance between simplicity and output. More complex models might give you more accurate results, but generally take more time to get right. We decided to predict the CPC for each platform separately for two reasons: First, the characteristics of each platform are different so blending the platforms also meant reducing insights.
Second, we decided to include an anomaly detection model which allowed us to give more specific alerts to the user. Each platform has noticeably different characteristics, as shown in the graph below. No model today will be able to predict the future perfectly.
The further in the future your prediction is, the wider the bandwidth becomes. The screenshot below provides a relevant representation. Our algorithm has generic parameters or settings the same for all clients and specific parameters per platform. These platform specific parameters are still the same for all our clients, but we are working on more flexibility. Building an algorithm is never a one-off activity, it needs to be part of your long term strategy.
In the screenshots below, you can see some examples of the Cervinodata CPC prediction and how it relates to reality. The solid line shows the actual CPC for the last couple of weeks, the dotted line above and below the filled line shows the previous prediction. As you can see, it was accurate in some cases and inaccurate in others. A miss in prediction can usually be explained by a change in budget after the prediction was executed.
Even with a great model, it is still a good idea to keep using your own brain power and gut feeling when interpreting the data. There will always be context that has an impact on the outcome of the algorithm that was not taken into account in the model.
Think, for instance, about a major news event, an exceptionally hot day, etc. Trusting your intuition will allow you to spot interesting variables that you can add to later versions of your model and improve its accuracy. Once you have the final output you need, it is wise to think hard about how and where you want to present it.
There are many dashboard solutions out there yet we still prefer Klipfolio for this. Klipfolio offers both TV screen view, desktop view, and mobile view and has nifty indicators that allow you to highlight specific parts of your data that need extra attention. After we ran multiple successful tests and shared the results with our clients we were confident enough to start working on the right infrastructure that allows us to continuously run the algorithm, without manual work.
Bringing your algorithm to a place that is robust and permanent is easier said than done. Is this solution similar to the solution to another problem? How are they alike? How are they different? This section contains an extended example that demonstrates the algorithm development process. To complete the algorithm, we need to know that every Jeroo can hop forward, turn left and right, pick a flower from its current location, and plant a flower at its current location.
A Jeroo starts at 0, 0 facing East with no flowers in its pouch. There is a flower at location 3, 0. Write a program that directs the Jeroo to pick the flower and plant it at location 3, 2.
After planting the flower, the Jeroo should hop one space East and stop. There are no other nets, flowers, or Jeroos on the island. The high-level algorithm partitioned the problem into three rather easy subproblems.
This seems like a good technique. This algorithm solves a very specific problem because the Jeroo and the flower are in very specific locations. This algorithm is actually a solution to a slightly more general problem in which the Jeroo starts anywhere, and the flower is 3 spaces directly ahead of the Jeroo.
A good programmer doesn't write a program all at once. Instead, the programmer will write and test the program in a series of builds. Each build adds to the previous one. The high-level algorithm will guide us in this process. To see this solution in action, create a new Greenfoot4Sofia scenario and use the Edit Palettes Jeroo menu command to make the Jeroo classes visible. Right-click on the Island class and create a new subclass with the name of your choice.
This subclass will hold your new code. The instantiation at the beginning of myProgram places bobby at 0, 0 , facing East, with no flowers. Once the first build is working correctly, we can proceed to the others. In this case, each build will correspond to one step in the high-level algorithm. It may seem like a lot of work to use four builds for such a simple program, but doing so helps establish habits that will become invaluable as the programs become more complex.
This build adds the logic to "get the flower", which in the detailed algorithm step 4 above consists of hopping 3 times and then picking the flower. The new code is indicated by comments that wouldn't appear in the original they are just here to call attention to the additions. The blank lines help show the organization of the logic. By taking a moment to run the work so far, you can confirm whether or not this step in the planned algorithm works as expected.
This build adds the logic to "put the flower". New code is indicated by the comments that are provided here to mark the additions. This build adds the logic to "hop East". There are two Jeroos. One Jeroo starts at 0, 0 facing North with one flower in its pouch.
The second starts at 0, 2 facing East with one flower in its pouch. There is a net at location 3, 2. Write a program that directs the first Jeroo to give its flower to the second one. After receiving the flower, the second Jeroo must disable the net, and plant a flower in its place.
After planting the flower, the Jeroo must turn and face South. One flower must be used to disable the net. The other flower must be planted at the location of the net, i. Each Jeroo will finish with 0 flowers in its pouch. One flower was used to disable the net, and the other was planted. This algorithm solves a very specific problem, but the specific locations are not important.
The only thing that is important is the starting location of the Jeroos relative to one another and the location of the net relative to the second Jeroo's location and direction. As before, the code should be written incrementally as a series of builds. Four builds will be suitable for this problem. As usual, the first build will contain the main method, the declaration and instantiation of the Jeroo objects, and the high-level algorithm in the form of comments.
The second build will have Ann give her flower to Andy. Whether you put on a jacket might depend on the temperature, and which jacket you choose might depend on the forecast. After picking your clothes, you then need to put them on. This is a key part of our algorithm. Finally, the last step of an algorithm is output — expressing the answer.
To a computer, output is usually more data, just like input. It allows computers to string algorithms together in complex fashions to produce more algorithms. However, output can also involve presenting information, for example putting words on a screen, producing auditory cues or some other form of communication.
So after getting dressed you step out into the world, ready for the elements and the gazes of the people around you. Maybe you even take a selfie and put it on Instagram to strut your stuff. Machine learning is commonplace for things like recommendations, predictions and looking up information.
For our getting-dressed example, a machine learning algorithm would be the equivalent of your remembering past decisions about what to wear, knowing how comfortable you feel wearing each item, and maybe which selfies got the most likes, and using that information to make better choices.
So, an algorithm is the process a computer uses to transform input data into output data.
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