I have a small dataset from 2006 to 2023, I would like to predict monthly sales for the next year. This is my data:

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I already tried Prophet and NeuralProphet, but unfortunately they don't work well after many trials with different parameters, I am doing my internship, so I don't have so much experience in this field, it would be great if experts give me some recommendation which algorithms can give me better results ? I was thinking about LSTM, but I think it needs many sample data for training. I am using python for programming.

  • $\begingroup$ You can start even more basic with an autocorrelation and then autoregression models, especially if you have a smaller dataset before jumping to LSTMs. $\endgroup$
    – m13op22
    Commented Feb 8 at 20:05
  • $\begingroup$ Just found an even better comparison between ARIMA, Prophet, and LSTM $\endgroup$
    – m13op22
    Commented Feb 8 at 20:09

1 Answer 1


A few years ago a blog post criticizing Prophet became very popular: https://www.microprediction.com/blog/prophet. One of the Prophet authors commented on the issue, basically agreeing with the post in this Twitter thread

The author of the post talks a lot about time series forecasting on his blog (https://www.microprediction.com/blog), and maintains a ranking with comparisons between different approaches (https://www.microprediction.com/ blog/fast). I think you can get ideas from there.

In general, Python people seem to tend to use pmdarima or other packages that have an auto-ARIMA implementation, while R people tend to use auto.arima from the forecast package.


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