I believe there is no need to emphasize the importance of reliable predictions of time series. Decades of research on the subject, two main approaches surviving: statistical (ARIMA, SARIMA, Box-Jenkins, Holt-Winter , ...) and neural (TLNN, CNN, recurrent networks, LSTM, ... ). Each one has its merits, otherwise there wouldn't be two: if you choose statistics you know the what and the why of every number you get, if you go neural you possibly get better results, but unjustified. This article follows neither approach, there is plenty of good courses teaching how to make serious forecasting, and even articles on Code Project ([1], [2]). This article presents a quick and dirty way to get something, knowing that one can do better. The associated code is quite simple and completely self-contained. I wrote it in javascript just because, but it is straightforward to move it to other languages. And it also easy to do the same processing in excel, in fact you can download from here the same in excel.

Hasty Time Series Prediction

maniezzo
Conceptualization
2018

Abstract

I believe there is no need to emphasize the importance of reliable predictions of time series. Decades of research on the subject, two main approaches surviving: statistical (ARIMA, SARIMA, Box-Jenkins, Holt-Winter , ...) and neural (TLNN, CNN, recurrent networks, LSTM, ... ). Each one has its merits, otherwise there wouldn't be two: if you choose statistics you know the what and the why of every number you get, if you go neural you possibly get better results, but unjustified. This article follows neither approach, there is plenty of good courses teaching how to make serious forecasting, and even articles on Code Project ([1], [2]). This article presents a quick and dirty way to get something, knowing that one can do better. The associated code is quite simple and completely self-contained. I wrote it in javascript just because, but it is straightforward to move it to other languages. And it also easy to do the same processing in excel, in fact you can download from here the same in excel.
2018
maniezzo
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/640033
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact