TimeCopilot Forecaster (Anomaly Detection)¶

Import libraries¶

In [1]:

%load_ext autoreload
%autoreload 2

%load_ext autoreload %autoreload 2

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)

In [24]:

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

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

Out[24]:

	unique_id	ds	y
0	Taylor Swift	2022-07-01	21499
1	Taylor Swift	2022-07-02	16349
2	Taylor Swift	2022-07-03	15042
3	Taylor Swift	2022-07-04	14358
4	Taylor Swift	2022-07-05	18332
...	...	...	...
6577	Selena Gomez	2025-06-27	8348
6578	Selena Gomez	2025-06-28	8670
6579	Selena Gomez	2025-06-29	9167
6580	Selena Gomez	2025-06-30	7902
6581	Selena Gomez	2025-07-01	7405

6582 rows × 3 columns

Import the models you want to use¶

In [25]:

from timecopilot.models.stats import SeasonalNaive, Theta
from timecopilot.models.foundation.chronos import Chronos
from timecopilot.models.foundation.flowstate import FlowState

from timecopilot.models.stats import SeasonalNaive, Theta from timecopilot.models.foundation.chronos import Chronos from timecopilot.models.foundation.flowstate import FlowState

Create a TimeCopilotForecaster¶

In [28]:

tcf = TimeCopilotForecaster(
    models=[
        Chronos(repo_id="amazon/chronos-bolt-mini"),
        FlowState(),
        SeasonalNaive(),
        Theta(),
    ]
)

tcf = TimeCopilotForecaster( models=[ Chronos(repo_id="amazon/chronos-bolt-mini"), FlowState(), SeasonalNaive(), Theta(), ] )

Plot the data¶

In [29]:

tcf.plot(df)

tcf.plot(df)

Out[29]:

Detect anomalies¶

You can optionally specify the following parameters:

• freq: The frequency of your data (e.g., 'D' for daily, 'M' for monthly). If you skip it, it will be inferred by default.
• h: The forecast horizon, which is the number of periods to predict during cross validation (you can skip it, and it will use an inferred seasonality)

In [ ]:

anomalies_df = tcf.detect_anomalies(df=df, h=7, level=99)

anomalies_df = tcf.detect_anomalies(df=df, h=7, level=99)

In [31]:

anomalies_df

anomalies_df

Out[31]:

	unique_id	ds	cutoff	y	Chronos	Chronos-lo-99	Chronos-hi-99	Chronos-anomaly	FlowState	FlowState-lo-99	FlowState-hi-99	FlowState-anomaly	SeasonalNaive	SeasonalNaive-lo-99	SeasonalNaive-hi-99	SeasonalNaive-anomaly	Theta	Theta-lo-99	Theta-hi-99	Theta-anomaly
0	Beyoncé	2022-07-06	2022-07-05	11571	11145.848633	-26105.343962	48397.041228	False	11127.576172	-25824.447434	48079.599778	False	11633.0	-30482.516525	53748.516525	False	10975.327148	-31700.535951	53651.190248	False
1	Beyoncé	2022-07-07	2022-07-05	11046	11386.040039	-25865.152556	48637.232634	False	10919.440430	-26032.583176	47871.464035	False	11633.0	-30482.516525	53748.516525	False	10399.726562	-32276.136537	53075.589662	False
2	Beyoncé	2022-07-08	2022-07-05	10874	11603.713867	-25647.478728	48854.906462	False	10822.110352	-26129.913254	47774.133957	False	11633.0	-30482.516525	53748.516525	False	9824.126953	-32851.736146	52499.990052	False
3	Beyoncé	2022-07-09	2022-07-05	10471	11858.917969	-25392.274626	49110.110563	False	10851.082031	-26100.941575	47803.105637	False	11633.0	-30482.516525	53748.516525	False	9248.526367	-33427.336732	51924.389466	False
4	Beyoncé	2022-07-10	2022-07-05	10721	12076.591797	-25174.600798	49327.784392	False	10776.775391	-26175.248215	47728.798996	False	11633.0	-30482.516525	53748.516525	False	8672.926758	-34002.936341	51348.789857	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6547	Taylor Swift	2025-06-27	2025-06-24	17837	21935.486328	-78538.233969	122409.206625	False	23186.416016	-74337.494379	120710.326411	False	21965.0	-108196.537131	152126.537131	False	21373.949219	-111072.688801	153820.587238	False
6548	Taylor Swift	2025-06-28	2025-06-24	17536	21935.486328	-78538.233969	122409.206625	False	22668.628906	-74855.281489	120192.539301	False	21965.0	-108196.537131	152126.537131	False	21367.833984	-111078.804035	153814.472004	False
6549	Taylor Swift	2025-06-29	2025-06-24	17960	21935.486328	-78538.233969	122409.206625	False	22706.308594	-74817.601801	120230.218989	False	21965.0	-108196.537131	152126.537131	False	21361.716797	-111084.921223	153808.354817	False
6550	Taylor Swift	2025-06-30	2025-06-24	15490	22124.191406	-78349.528891	122597.911703	False	22523.058594	-75000.851801	120046.968989	False	21965.0	-108196.537131	152126.537131	False	21355.601562	-111091.036457	153802.239582	False
6551	Taylor Swift	2025-07-01	2025-06-24	15127	22124.191406	-78349.528891	122597.911703	False	21546.013672	-75977.896723	119069.924067	False	21965.0	-108196.537131	152126.537131	False	21349.484375	-111097.153645	153796.122395	False

6552 rows × 20 columns

Plot results¶

In [32]:

tcf.plot(df, anomalies_df)

tcf.plot(df, anomalies_df)

Out[32]: