I was working on the assignment to build a large scale time-series prediction solution. I end up using a combination of approaches in the single solution — Prophet, ARIMA and LSTM Neural Network (running on top of Keras/TensorFlow). With Prophet (Serving Prophet Model with Flask — Predicting Future) and ARIMA it is straightforward to calculate a prediction for future dates, both provide a function to return prediction for a given future horizon. The same is not obvious with LSTM, if you are new — this will require a significant amount of time to research how to forecast true future dates (most of the examples are showing how to predict against test dataset only).
I found one good example though which I was following and it helped me to solve my task — A Quick Example of Time-Series Prediction Using Long Short-Term Memory (LSTM) Networks. In this post, I will show how to predict shampoo sales monthly data, mainly based on the code from the above example.
Read more - Time-Series Prediction Beyond Test Data.
I found one good example though which I was following and it helped me to solve my task — A Quick Example of Time-Series Prediction Using Long Short-Term Memory (LSTM) Networks. In this post, I will show how to predict shampoo sales monthly data, mainly based on the code from the above example.
Read more - Time-Series Prediction Beyond Test Data.
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