Using XGBoost for Time Series Forecasting in Python ❌ XGBoost for Stock Price Prediction Tutorial

No video

Using XGBoost for Time Series Forecasting in Python ❌ XGBoost for Stock Price Prediction Tutorial

Рет қаралды 37,893

DecisionForest

Күн бұрын

Пікірлер: 63

@DecisionForest 3 жыл бұрын

Hi there! If you want to stay up to date with the latest machine learning and deep learning tutorials subscribe below. Thank you for your support! kzfaq.info

@vivekrai1974 2 жыл бұрын

10:15 Explanation of Walk Forward Method

@dusanbosnjakovic6588 3 жыл бұрын

Just so I understand: This method 1) doesn't de-trend the data or make it stationary, 2) uses just the current price as the only feature (no rolling window aggregates), correct?

@Spartanboy123 3 жыл бұрын

Great Information. Thank you for your time!! Once you find the rmse for your train/test model. How can you predict for next 2 years?

@limyong8823 Жыл бұрын

Excellent explaination of XGBoost and it's slimplifed things.

@weiyang2116 3 жыл бұрын

Great video.. How do you cater for the case where there's a new major event like the pandemic, because the old trained model wouldn't work anymore? Thanks!

@wojtek8396 3 жыл бұрын

why do you put val into np.array? Why do not you use encoders and scaler before testing?

@vuduy9763 3 жыл бұрын

you save my day!! thank you from VietNam

@charlotteramos2267 3 жыл бұрын

You're a great teacher!! Thanks for the lesson, ir really helped me!! 🙂

@DecisionForest 3 жыл бұрын

Thank you so much Charlotte, glad it was useful.

@hartejhaer8851 3 жыл бұрын

Can you show the end prediction results please? Would be very useful as a reference point :)

@brandaphoon2502 3 жыл бұрын

I am a student and this helped me greatly! Is there a video for incorporating technical indicators or other features to have a more accurate model?

@DecisionForest 3 жыл бұрын

Happy it helped. I haven't created any video with technical indicators as features as it's pretty easy to extend my current videos with them. Let me know what results you get if you try it out. My results so far have been pretty poor with standard indicators although I wasn't surprised :)

@hasithahiranrajapaksa5611 3 жыл бұрын

hey,i have a question. can we measure the r2Score of this? I got a negative r2score. Can I know what is the reason?

@dishydez 2 жыл бұрын

Great video! Another tool in the ML toolbox. Is this different to the Timeseries Cross validation?

@morecharacterswithamix9067 2 жыл бұрын

Nice! I have a time series problem where the data is at daily level for 3 years and we forecast for daily level 2 years out. Currently I use Prophet which works well. Prophet takes care of spike events (holidays) and weekly / yearly seasonality. Can I use XGBoost to do this? Your example here is forecasting one step out which will be like forecasting one day out for my case.

@scienceforyoung 3 жыл бұрын

Good video. Is there a way to predict the unknown future? or how can we adapt the function to this approach? Thank you.

@roywit7836 3 жыл бұрын

This looks great thanks :) just subbed today, loving your channel

@DecisionForest 3 жыл бұрын

Thank you for the support! Glad you find the content helpful.

@Borzacchinni Жыл бұрын

Great demonstration!

@stephenyates1730 3 жыл бұрын

Thanks for sharing.... Keep getting an error at the last line after print(rmse) ... ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

@emeline894 3 жыл бұрын

Thank you for this very helpful video. Much appreciated!

@DecisionForest 3 жыл бұрын

Happy I could help!

@ddrikee2546 Жыл бұрын

Do you have relevant experience in machine learning survival analysis? For example xgbse , scikit survival and pycox package in python. I don't know how the data format is handled in this case, also time dependent ROC, calibration and DCA.

@RevenueRocketeers 2 жыл бұрын

I didn't understand the val object,why did u reshape it.Plz give a clarity. Thanks

@malcolmburr6602 3 жыл бұрын

@DecisionForest Great Video! Is it common for the model to take over 15 minutes? The validation walkthrough part to be exact. Mine has been running for a good amount of time.

@DecisionForest 3 жыл бұрын

Thanks Malcolm! Yes, the walk forward validation and any type of validation can take quite a long time depending on computing power.

@brianchaplin278 8 ай бұрын

Nice explanation

@shadialmoayed1262 3 жыл бұрын

Thanks, that was helpful.. can you provide more videos on XAI (Shap, Lime, etc..) and how to inerpret models such as XGBoost, RandomForest, Deep learning NN models and so on

@DecisionForest 3 жыл бұрын

Glad it was helpful. I wanted to do a video on SHAP, I actually created the notebook just that it takes forever to run, it's extremely computationally expensive especially with high dimensional datasets. I'd encourage you to use LIME as it's more practical for real world scenarios even if SHAP may be more interesting on paper.

@salehalatwah9002 3 жыл бұрын

looks good , the question is how can you predict into unknown future ? how do you give it number of days to keep looping on training updating prediction and retraining

@DecisionForest 3 жыл бұрын

That's exactly what we're doing here with walk forward validation, we keep retraining and predicting. That's why walk forward validation is so important for time series.

@prosimulate 3 жыл бұрын

You’re great Sir!

@DecisionForest 3 жыл бұрын

You’re kind, thank you! Glad it was helpful.

@anurag931983 2 жыл бұрын

I read in a research paper to predict time series we may use multiple trajectories. Any insight on what these trajectories are in time series and how to calculate these?

@jimmyrico5364 3 жыл бұрын

Good video, thanks for sharing this! Could you maybe indicate why can't you after fitting the model just pass the full X_test set and use the predict method available from XGBoost?

@DecisionForest 3 жыл бұрын

Glad it was helpful. Well that’s the trick and why walk forward validation is useful. why would you ignore new data when you have it, of course you could but you want to get the best possible results.

@midnight6371 3 жыл бұрын

2:52 what is the purpose of debuggers ?

@ragendhusr5791 3 жыл бұрын

Thankyou for sharing. I got error in pred = xgb_predict(history, test_X[0]) and X, y = train[:, :-1], train[:, -1]. The error message was "IndexError: too many indices for array: array is 0-dimensional, but 2 were indexed". Could you please tell me how to resolve this error?

@saurabhkumar289 3 жыл бұрын

Amazing content thanks sir

@DecisionForest 3 жыл бұрын

Most welcome, happy for your support

@Wolf-hh4rv 2 жыл бұрын

Just read a raft of papers telling me that tree based methods are not easily adapted to time series… if you give them the simplest model ie extrapolation, can’t do. Dude you do this for a living.

@DecisionForest 2 жыл бұрын

of course there are, and there are others claiming the opposite. We start with a theory, test it in practice and based on the results we decide what is good and what isn't. Papers are incredibly useful as they help you avoid wasting time but in the end what works in the real world matters.

@varungupta4228 3 жыл бұрын

Hi! Great video. But this was only the validation of the model. Suppose we want to forecast it further. How do we do that?

@DecisionForest 3 жыл бұрын

Cheers Varun! Well after you are satisfied with the results, you train on the full dataset.

@varungupta4228 3 жыл бұрын

@@DecisionForest Hi Thanks for replying. So once we do a walk forward validation on the train test split, I finalize my model. I use that model on the whole dataset. I am not able to understand what will the model return at that step?

@lollmao249 2 жыл бұрын

i tried to sign up to get the code but failed why ?

@malleswararaomaguluri6344 3 жыл бұрын

I have 450 points for train data, need to predict for next 10 samples, how can we do train test split

@sunaxes Жыл бұрын

Your model is using one hour's value to predict the next? This is ridiculously useless model haha. But the explanations are great!

@rrmusic970 3 жыл бұрын

This tutorial video is crystal clear to me up until 13:59 minute, then i lost completly... What is the meant of 1.7967091070446082 ??? Is it the prediction of the next hour close price??? Or have i miss something??? i mean, how to print out that predictions for the net 1 hour close price???

@DecisionForest 3 жыл бұрын

Thanks, glad it was helpful. That is the RMSE, the root mean squared error, the metric that we want to optimize in this case. You need to optimise for small values of this metric. Hope this helped.