so timesfm is used to forecast the future time series data based on current time series data?
@nodematic
16 күн бұрын
Exactly
@陈永祥-p7v
Ай бұрын
what this app or web for drawing
@nodematic
Ай бұрын
That is softwaresim.com
@fathimashaniya9805
3 ай бұрын
Hi, Thank you very much. Your vedio was very beneficial.
@soumyaaaaa-z9k
3 ай бұрын
Hi thanks for your great video! I have a question, is there a way to install the timesFM in my local system in python either using Jupyter or VScode. I have been trying to install it but failing to do so, it fails because of the Paxml library.
@nodematic
2 ай бұрын
I can replicate an issue like this, by using an older Python version like 3.9.2. Only Python 3.10+ can install the paxml>=1.4.0 that is required for timesfm. If you're using Python 3.9, can you try 3.10 or newer?
@ziqili2120
3 ай бұрын
Hi, thanks for your great video! Do you know how to finetune this model on some private dataset? I found the tutorials using Paxml framework are very limited. I've been stuck in the finetuning for weeks.
@nodematic
3 ай бұрын
It doesn't look like this is very well supported right now, but the model architecture means this should be relatively easy once it's better documented/supported. We'll try to create a video on this when the fine-tuning experience is improved.
@fathimashaniya9805
2 ай бұрын
If I want to consider other features also for training…. What should be my code…??
@fathimashaniya9805
2 ай бұрын
Please can you give me example code to use the rest of the features also…. Also..in case there is a date col … while training do we have to include the month col week col etc separately? If so what should be the dtype of them? Also other if there are cols like id, state id , category id,…. Other id columns , do we consider them while training?
@fathimashaniya9805
3 ай бұрын
Can I ask u another question? How do I fine tune this model
@nodematic
3 ай бұрын
Fine-tuning does not appear to be supported right now, but they may add support for that in the future, since the model architecture lends itself well to fine-tuning.
@katarzynakuryo197
3 ай бұрын
Hi, do you know if it is possible to use this model for multivariate time series?
@nodematic
3 ай бұрын
Yes, the model is multivariate. Just be sure to include everything in the forecast call "inputs".
@katarzynakuryo197
3 ай бұрын
@@nodematic I want to train my model on both ts at once. Do you know why it is not working? data = pd.DataFrame({ 'ds': train_df.index, 'ts1': train_df["ts1"], 'ts2': train_df["ts2"], 'unique_id': 'sensor_1' }) forecast_df=tfm.forecast_on_df( inputs=data, freq="D", value_name=["ts1", "ts2"] )
@camillacarpinelli7531
2 ай бұрын
@@katarzynakuryo197 ts1 and ts2 have to be unique ids, you need to have your dataset like [["ds","sales","unique_id"] that means that you should have date = ds, quantity sold = sales, unique_id = ts1,ts2,ts3 etc. You need to change your dataset in order to have only these three columns
@camillacarpinelli7531
2 ай бұрын
of course this means that your date is not unique anymore because in a single day you could have sold multiple products but it doesn't matter.
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