Merge pull request #102 from google-research/rajat_dev

update readme to reflect recent feature updates.

README.md

@@ -16,6 +16,11 @@ This is not an officially supported Google product.
 
 We recommend at least 16GB RAM to load TimesFM dependencies.
 
+## Update - July 15, 2024
+
+- Launched [finetuning support](https://github.com/google-research/timesfm/blob/master/notebooks/finetuning.ipynb) that lets you finetune the weights of the pretrained TimesFM model on your own data.
+- Launched [~zero-shot covariate support](https://github.com/google-research/timesfm/blob/master/notebooks/covariates.ipynb) with external regressors.
+
 ## Checkpoint timesfm-1.0-200m
 
 timesfm-1.0-200m is the first open model checkpoint:
@@ -195,6 +200,49 @@ forecast_df = tfm.forecast_on_df(
 )
 ```
 
+## Covariates Support
+
+We now have an external regressors library on top of TimesFM that can support static covariates as well as dynamic covariates available in the future. We have an usage example in `notebooks/covariates.ipynb`.
+
+Let's take a toy example of forecasting sales for a grocery store: 
+
+**Task:** Given the observed the daily sales of this week (7 days), forecast the daily sales of next week (7 days).
+
+```
+Product: ice cream
+Daily_sales: [30, 30, 4, 5, 7, 8, 10]
+Category: food
+Base_price: 1.99
+Weekday: [0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6]
+Has_promotion: [Yes, Yes, No, No, No, Yes, Yes, No, No, No, No, No, No, No]
+Daily_temperature: [31.0, 24.3, 19.4, 26.2, 24.6, 30.0, 31.1, 32.4, 30.9, 26.0, 25.0, 27.8, 29.5, 31.2]
+```
+
+```
+Product: sunscreen
+Daily_sales: [5, 7, 12, 13, 5, 6, 10]
+Category: skin product
+Base_price: 29.99
+Weekday: [0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6]
+Has_promotion: [No, No, Yes, Yes, No, No, No, Yes, Yes, Yes, Yes, Yes, Yes, Yes]
+Daily_temperature: [31.0, 24.3, 19.4, 26.2, 24.6, 30.0, 31.1, 32.4, 30.9, 26.0, 25.0, 27.8, 29.5, 31.2]
+```
+
+In this example, besides the `Daily_sales`, we also have covariates `Category`, `Base_price`, `Weekday`, `Has_promotion`, `Daily_temperature`. Let's introduce some concepts:
+
+**Static covariates** are covariates for each time series. 
+- In our example, `Category` is a **static categorical covariate**, 
+- `Base_price` is a **static numerical covariates**.
+
+**Dynamic covariates** are covaraites for each time stamps.
+- Date / time related features can be usually treated as dynamic covariates.
+- In our example, `Weekday` and `Has_promotion` are **dynamic categorical covariates**.
+- `Daily_temperate` is a **dynamic numerical covariate**.
+
+**Notice:** Here we make it mandatory that the dynamic covariates need to cover both the forecasting context and horizon. For example, all dynamic covariates in the example have 14 values: the first 7 correspond to the observed 7 days, and the last 7 correspond to the next 7 days.
+
+We can now provide the past data of the two products along with static and dynamic covariates as a batch input to TimesFM and produc forecasts that take into the account the covariates. To learn more, check out the example in `notebooks/covariates.ipynb`.
+
 ## Finetuning
 
 We have provided an example of finetuning the model on a new dataset in `notebooks/finetuning.ipynb`.