@ninjaturtle205
thank you thank you. This information is skipped out in most machine learning courses, and no one will teach you this. In practice, a lot of data has temporal nature, while all along you only learned how to classify cats and dogs, and regress house pricess.
@julien957
Genius. He Makes python and time series almost easy to understand.
@olegkazanskyi9752
This is a truly useful session. Thank you for sharing the knowledge!
@Tntpker
Really useful fricking video. MUST WATCH for any data sci/ml engineer.
@anirudhsharma3879
Amazing dump of knowledge, I have multiple times came back to this video
@calebterrelorellana2478
Solo un maestro explica en sencillo y de forma visual temas complejos! gracias! la mejor exposición de forecasting!
@youknowmyname12345
Very good talk. The presenter is a great teacher!
@wolpumba4099
Abstract This talk explores how to adapt machine learning models for time series forecasting by transforming time series data into tabular datasets with features and target variables. Kishan Manani discusses the advantages of using machine learning for forecasting, including its ability to handle complex data structures and incorporate exogenous variables. He then dives into the specifics of feature engineering for time series, covering topics like lag features, window features, and static features. The talk emphasizes the importance of avoiding data leakage and highlights the differences between machine learning workflows for classification/regression and forecasting tasks. Finally, Manani introduces useful libraries like Darts and sktime that facilitate time series forecasting with tabular data and provides practical examples. Summary Why use machine learning for forecasting? (1:25) - Machine learning models can learn across many related time series. - They can effectively incorporate exogenous variables. - They offer access to techniques like sample weights and custom loss functions. Don't neglect simple baselines though! (3:45) - Simple statistical models can be surprisingly effective. - Ensure the uplift from machine learning justifies the added complexity. Forecasting with machine learning (4:15) - Convert time series data into a table with features and a target variable. - Use past values of the target variable as features, ensuring no data leakage from the future. - Include features with known past and future values (e.g., marketing spend). - Handle features with only past values (e.g., weather) by using alternative forecasts or lagged versions. - Consider static features (metadata) to capture differences between groups of time series. Multi-step forecasting (8:07) - Direct forecasting: Train separate models for each forecast step. - Recursive forecasting: Train a one-step ahead model and use it repeatedly, plugging forecasts back into the target series. Cross-validation: Tabular vs Time series (11:32) - Randomly splitting data is inappropriate for time series due to temporal dependence. - Split data by time, replicating the forecasting process for accurate performance evaluation. Machine learning workflow (13:00) - Time series forecasting workflow differs significantly from classification/regression tasks. - Feature engineering and handling vary at predict time depending on the multi-step forecasting approach. Feature engineering for time series forecasting (14:47) - Lag features: Use past values of target and features, including seasonal lags. - Window features: Compute summary statistics (e.g., mean, standard deviation) over past windows. - Nested window features: Capture differences in various time scales. - Static features: Encode categorical metadata using target encoding, being mindful of potential target leakage. Overview of some useful libraries (27:01) - tsfresh: Creates numerous time series features from a data frame. - Darts and sktime: Facilitate forecasting with tabular data and offer functionalities like recursive forecasting and time series cross-validation. Forecasting with tabular data using Darts (28:04) - Example demonstrates forecasting with lag features and future known features on single and multiple time series. disclaimer: i used gemini 1.5 pro to summarize the youtube transcript.
@HEYTHERE-ko6we
This is by far one of the best wholesome videos on time series forecasting!!! loved it
@onuragmaji
Great talk hope will get more contents like that on Practical TS
@ChandanNayak-i8s
Excellent presentation. Great work Kishan
@Fan-fb4tz
amazing talk to time series beginners like me!
@蔡传泽
dude is a PhD for a reason, awesome stuff god damn
@zakkyang6476
finally, someone can articulate this topic well...
@duscio
Great Presentation ! Interesting and clear
@laizerLL572
Hi Am so grateful for this tutorial
@adityaghuse374
Great work👍👍
@5112vivek
I will checkout these libraries. Very informative, thanks
@蔡传泽
this is some sysly good stuff!
@lashlarue7924