TimeCopilot Forecaster¶

Import libraries¶

In [2]:

import pandas as pd
from timecopilot import TimeCopilotForecaster

import pandas as pd from timecopilot import TimeCopilotForecaster

Load the dataset.¶

The DataFrame must include at least the following columns:

• unique_id: Unique identifier for each time series (string)
• ds: Date column (datetime format)
• y: Target variable for forecasting (float format)

The pandas frequency will be inferred from the ds column, if not provided. If the seasonality is not provided, it will be inferred based on the frequency. If the horizon is not set, it will default to 2 times the inferred seasonality.

In [3]:

df = pd.read_csv(
    "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv",
    parse_dates=["ds"],
)
df.head()

df = pd.read_csv( "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv", parse_dates=["ds"], ) df.head()

Out[3]:

	unique_id	ds	y
0	AirPassengers	1949-01-01	112
1	AirPassengers	1949-02-01	118
2	AirPassengers	1949-03-01	132
3	AirPassengers	1949-04-01	129
4	AirPassengers	1949-05-01	121

Import the models you want to use¶

In [9]:

from timecopilot.models.benchmarks.prophet import Prophet
from timecopilot.models.benchmarks.stats import AutoARIMA, AutoETS, SeasonalNaive
from timecopilot.models.foundational.moirai import Moirai

from timecopilot.models.benchmarks.prophet import Prophet from timecopilot.models.benchmarks.stats import AutoARIMA, AutoETS, SeasonalNaive from timecopilot.models.foundational.moirai import Moirai

Create a TimeCopilotForecaster¶

In [10]:

tcf = TimeCopilotForecaster(
    models=[
        AutoARIMA(), 
        AutoETS(), 
        Moirai(), 
        Prophet(), 
        SeasonalNaive(),
    ]
)

tcf = TimeCopilotForecaster( models=[ AutoARIMA(), AutoETS(), Moirai(), Prophet(), SeasonalNaive(), ] )

Generate forecast¶

You can optionally specify the following parameters:

• freq: The frequency of your data (e.g., 'D' for daily, 'M' for monthly)
• h: The forecast horizon, which is the number of periods to predict
• seasonality: The seasonal period of your data, which can be inferred if not provided

In [ ]:

fcst_df = tcf.forecast(df=df, h=12, level=[80, 90])

fcst_df = tcf.forecast(df=df, h=12, level=[80, 90])

1it [00:00,  1.96it/s]
11:34:12 - cmdstanpy - INFO - Chain [1] start processing
11:34:12 - cmdstanpy - INFO - Chain [1] done processing

Plot results¶

In [14]:

tcf.plot(df, fcst_df, level=[80, 90])

tcf.plot(df, fcst_df, level=[80, 90])

Out[14]: