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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