User: what is future covariates?

from darts.datasets import AirPassengersDataset
from darts.models import KalmanForecaster
from darts.utils.timeseries_generation import datetime_attribute_timeseries
series = AirPassengersDataset().load()
# optionally, use some future covariates; e.g. the value of the month encoded as a sine and cosine series
future_cov = datetime_attribute_timeseries(series, "month", cyclic=True, add_length=6)
# increasing the size of the state vector
model = KalmanForecaster(dim_x=12)
model.fit(series, future_covariates=future_cov)
pred = model.predict(6, future_covariates=future_cov)
pred.values()
array([[474.40680728],
       [440.51801726],
       [461.94512461],
       [494.42090089],
       [528.6436328 ],
       [590.30647185]])
Assistant: In time series forecasting, future covariates (also known as exogenous variables or external regressors) are additional variables that you believe might have an impact on the variable you're trying to predict. These covariates are provided as inputs to the forecasting model alongside the main time series data to potentially improve the model's accuracy.

For the "AirPassengers" dataset and the KalmanForecaster model you are using, you have chosen the "month" as a future covariate, encoded as sine and cosine components. This technique is common when dealing with cyclic or periodic patterns, such as seasonal effects. By encoding the month in this way, you're essentially providing the model with information about the cyclic nature of the data, which might help it make more informed forecasts.

Here's a breakdown of what's happening in your code:

1. **Data Loading**: You load the AirPassengers time series dataset, which contains monthly passenger numbers for an airline.

2. **Covariate Generation**: Use the `datetime_attribute_timeseries` utility to create a time series of cyclic covariates corresponding to months, with sine and cosine transformations. These transformations help in capturing the cyclic nature and allow the model to understand the repeating patterns (e.g., seasonality).

3. **Model Configuration**: A `KalmanForecaster` model is initialized with an increased state dimension (`dim_x=12`). This state vector's dimension influences how much past information the model can incorporate.

4. **Model Fitting and Prediction**: Fit the model to the data while providing both the primary time series and the future covariates. Then, predict future values by using these covariates to make better forecasts.

In summary, future covariates like the cyclic month representation can improve forecast accuracy by incorporating periodic effects into the modeling process.
User: can i use future covariate such as weather component like weather?
Assistant: Yes, you can use weather-related data as future covariates in your time series forecasting model if you believe they may have an impact on the variable you are trying to predict. This is particularly useful in scenarios where weather conditions can influence the target, such as predicting energy consumption, sales of seasonal products, or agricultural yields.

Here's how you might incorporate weather data as future covariates:

1. **Data Collection**: Gather relevant weather data corresponding to your time series timeline. This could include temperature, humidity, rainfall, wind speed, or any other weather-related variables that you think might affect your target variable.

2. **Data Alignment**: Ensure that your weather data is aligned with your main time series data in terms of time intervals. If your main data is monthly, you might need to aggregate daily weather data to monthly averages or totals.

3. **Feature Engineering**: Consider any transformations or encoding that might be useful, such as normalizing the data or creating lag variables if certain weather effects are delayed.

4. **Modeling**: When fitting your model, provide the weather data as future covariates. Ensure that your model can incorporate these exogenous variables, as not all time series models support this feature. The `KalmanForecaster`, like many models in the Darts library, allows for the inclusion of future covariates.

5. **Forecasting**: When making predictions, supply forecasts or projections of the same weather variables into the future. This is important because the model needs these values as inputs to generate future predictions.

Using weather data as covariates can enhance your model by allowing it to account for external factors that introduce additional variability or patterns into the time series, potentially leading to more accurate and robust forecasts.
User: what is dim_x?
Assistant: