V0.0.10
Features
- TimeCopilotForecaster Class: Introduced the
TimeCopilotForecaster
class to enhance forecasting capabilities. See #48. Example:
import pandas as pd
from timecopilot import TimeCopilotForecaster
from timecopilot.models.benchmarks import SeasonalNaive
from timecopilot.models.foundational import TimesFM
df = pd.read_csv(
"https://timecopilot.s3.amazonaws.com/public/data/algeria_exports.csv",
parse_dates=["ds"],
)
forecaster = TimeCopilotForecaster(models=[TimesFM(), SeasonalNaive()])
fcsts_df = forecaster.forecast(df=df, h=12, freq="MS")
- Probabilistic Forecasts: Added support for probabilistic forecasts in the forecaster class. See #50. Example:
import pandas as pd
from timecopilot import TimeCopilotForecaster
from timecopilot.models.benchmarks import SeasonalNaive, Prophet
from timecopilot.models.foundational import TimesFM
df = pd.read_csv(
"https://timecopilot.s3.amazonaws.com/public/data/algeria_exports.csv",
parse_dates=["ds"],
)
forecaster = TimeCopilotForecaster(models=[TimesFM(), SeasonalNaive()])
fcsts_df_level = forecaster.forecast(
df=df,
h=12,
freq="MS",
level=[80, 90],
)
fcsts_df_quantiles = forecaster.forecast(
df=df,
h=12,
freq="MS",
quantiles=[0.1, 0.9],
)
-
Integration with External Libraries:
-
Multi-series Support: Enhanced the agent to handle multiple time series. See #64.
- Example:
from timecopilot import TimeCopilot tc = TimeCopilot() # now the forecast method can handle multiple time series tc.forecast(...)
- Example:
-
Agent Integration: Utilized the TimeCopilotForecaster class within the agent. See #65.
Tests
- Basic Functionality Tests: Added tests for basic functionality to ensure reliability. See #43.
Fixes
- CI Improvements: Implemented a fix to cancel concurrent CI runs, optimizing the CI process. See #63.