maestral VS darts

maestral is an open source Mac/Linux client. Just one example, I’m sure there are others.

Worth mention the open source project Maestral: https://github.com/SamSchott/maestral

(an unofficial Dropbox client)

Sometime late last fall, my NAS rebooted and my ZFS pools didn't come back online automatically, but Docker did automatically restart the Maestral client. In so doing, it detected an empty folder...and...promptly deleted EVERYTHING in my Dropbox, assuming I'd just deleted everything locally. It's an issue that has had at least a few bug reports and which as of yet still hasn't been addressed - basically, the client needs but does not have a way to say "a bunch of stuff is changing, are you sure?" before doing it - especially on a restart of the client.

>- Debug apps which don't opt into debugging, using gdb/lldb, without disabling SIP. It's my computer, I should be root, I should be able to introspect how processes execute on it. Not being able to do so prevented me from debugging https://github.com/samschott/maestral/issues/597, since I had to disable SIP, which required rebooting, which stopped the bug from happening.

Another option is to re-sign the application with the entitlements necessary for debugging.

e.g:

https://gist.github.com/talaviram/1f21e141a137744c89e81b58f7...

there is a native build as beta, but I found Maestral much better :) https://github.com/samschott/maestral

3) No support for extended file attributes (so you cannot sync .app bundles)

[1] https://github.com/SamSchott/maestral/issues/443

If you still haven't found a use for them: they make for good low-power, always-on, headless networked servers for processing-light tasks.

Other commentators mentioned pihole/DNS, WireGuard, and music streaming, but you can also use them for a (slow) NFS server, persistent Syncthing node, Maestral host[1] (third-party Dropbox client written in Python (that can actually run on the Raspberry Pi, unlike the official Dropbox client)), or device that maintains a connection to a distributed network (e.g. Hyporborea/cjdns).

[1] https://github.com/SamSchott/maestral

I'd been reading up on Linux for the last little bit, learning about different distros and what things to look out for - so when this thing arrived I jumped onto the latest Fedora release & am really enjoying it. I've tweaked some Gnome extensions, gotten my favorite writing app downloaded, set up the awesome Maestral open-source Dropbox client, and set up a decent little undervolt using intel-undervolt, mprime, cinebench, and glmark2 :)

Open-source Dropbox client, with multi-account, no-device-limit and M1 support\ (147 comments)

Don't use an LSTM. Get up to date with SoTA methods and read the papers in the field. LSTMs are not the way forward. Read the papers I suggested. It would be very useful to come to grips with both the Time Series Repository (https://github.com/thuml/Time-Series-Library) and Darts (https://github.com/unit8co/darts) as these are widely used for research and in industry.

As others have pointed out, Prophet is not a particularly good model for forecasting, and has been superseded by a multitude of other models. If you want to do time series forecasting, I'd recommend using Darts: https://github.com/unit8co/darts. Darts implements a wide range of models and is fairly easy to use.

The problem with time series forecasting in general is that they make a lot of assumptions on the shape of your data, and you'll find you're spending a lot of time figuring out mutating your data. For example, they expect that your data comes at a very regular interval. This is fine if it's, say, the data from a weather station. This doesn't work well in clinical settings (imagine a patient admitted into the ER -- there is a burst of data, followed by no data).

That said, there's some interesting stuff out there that I've been experimenting with that seems to be more tolerant of irregular time series and can be quite useful. If you're interested in exchanging ideas, drop me a line (email in my profile).

3. darts

Looking forward to checking this out! How does this compare with darts[1]?

[1] https://unit8co.github.io/darts/

Darts is an open-source Python library by Unit8 for easy handling, pre-processing, and forecasting of time series. It contains an array of models, from standard statistical models such as ARIMA to deep neural networks. https://unit8co.github.io/darts/

active support

The new Forecast menu, featuring the open-source Darts Time Series library, offers script-friendly functionality. It's also easy to use. Don't have any data to load yet? Enter through the Stocks or Crypto menus.

I would recommend Darts in Python [1]. It's easy to use (think fit()/predict()) and includes

* Statistical models (ETS, (V)ARIMA(X), etc)

* ML models (sklearn models, LGBM, etc)

* Many recent deep learning models (N-BEATS, TFT, etc)

* Seamlessly works on multi-dimensional series

* Models can be trained on multiple series

* Many models offer rich support for probabilistic forecasts

* Model evaluation is easy: Darts has many metrics, offers backtest etc

* Deep learning scales to large datasets, using GPUs, TPUs, etc

* There's even now an explainability module for some of the models - showing you what matters for computing the forecasts

* (coming soon): an anomaly detection module :)

* (also, it even include FB Prophet if you really want to use it)

Warning: I'm probably biased because I'm Darts creator.

[1] https://github.com/unit8co/darts