Time Series¶

Infrastructure systems supports time series data expressed as a one-dimensional array of floats using the class infrasys.time_series_models.SingleTimeSeries. Users must provide a name that is typically the field of a component being modeled. For example, if the user has a time array associated with the active power of a generator, they would assign name = "active_power".

Here is an example of how to create an instance of infrasys.time_series_models.SingleTimeSeries:

    import random
    time_series = SingleTimeSeries.from_array(
        data=[random.random() for x in range(24)],
        name="active_power",
        initial_time=datetime(year=2030, month=1, day=1),
        resolution=timedelta(hours=1),
    )

Users can attach their own attributes to each time array. For example, there might be different profiles for different scenarios or model years.

    time_series = SingleTimeSeries.from_array(
        data=[random.random() for x in range(24)],
        name="active_power",
        initial_time=datetime(year=2030, month=1, day=1),
        resolution=timedelta(hours=1),
        scenario="high",
        model_year="2035",
    )

Deterministic Time Series¶

In addition to SingleTimeSeries, infrasys also supports deterministic time series, which are used to represent forecasts or scenarios with a known future.

The infrasys.time_series_models.Deterministic class represents a time series where the data is explicitly stored as a 2D array, with each row representing a forecast window and each column representing a time step within that window.

You can create a Deterministic time series in two ways:

Explicitly with forecast data using Deterministic.from_array() when you have pre-computed forecast values.
From a SingleTimeSeries using Deterministic.from_single_time_series() to create a “perfect forecast” based on historical data by extracting overlapping windows.

Creating Deterministic Time Series with Explicit Data¶

This approach is used when you have explicit forecast data available. Each forecast window is stored as a row in a 2D array.

Example:

import numpy as np
from datetime import datetime, timedelta
from infrasys.time_series_models import Deterministic
from infrasys.quantities import ActivePower

initial_time = datetime(year=2020, month=9, day=1)
resolution = timedelta(hours=1)
horizon = timedelta(hours=8)  # 8 hours horizon (8 values per forecast)
interval = timedelta(hours=1)  # 1 hour between forecasts
window_count = 3  # 3 forecast windows

# Create forecast data as a 2D array where:
# - Each row is a forecast window
# - Each column is a time step in the forecast horizon
forecast_data = [
    [100.0, 101.0, 101.3, 90.0, 98.0, 87.0, 88.0, 67.0],  # 2020-09-01T00 forecast
    [101.0, 101.3, 99.0, 98.0, 88.9, 88.3, 67.1, 89.4],  # 2020-09-01T01 forecast
    [99.0, 67.0, 89.0, 99.9, 100.0, 101.0, 112.0, 101.3],  # 2020-09-01T02 forecast
]

# Create the data with units
data = ActivePower(np.array(forecast_data), "watts")
name = "active_power_forecast"
ts = DeterministicTimeSeries.from_array(
# Create the data with units
data = ActivePower(np.array(forecast_data), "watts")
name = "active_power_forecast"
ts = Deterministic.from_array(
    data, name, initial_time, resolution, horizon, interval, window_count
)

Creating “Perfect Forecasts” from SingleTimeSeries¶

The from_single_time_series() classmethod is useful when you want to create a “perfect forecast” based on historical data for testing or validation purposes. It extracts overlapping forecast windows from an existing SingleTimeSeries.

Example:

from datetime import datetime, timedelta
from infrasys.time_series_models import Deterministic, SingleTimeSeries

initial_timestamp = datetime(year=2020, month=1, day=1)
name = "active_power"
ts = SingleTimeSeries.from_array(
    data=range(8784),
    name=name,
    resolution=timedelta(hours=1),
    initial_timestamp=initial_timestamp,
)
horizon = timedelta(hours=8)
interval = timedelta(hours=1)
ts_deterministic = Deterministic.from_single_time_series(
    ts, interval=interval, horizon=horizon
)

In this example, ts_deterministic creates a forecast for active_power by extracting forecast windows from the original SingleTimeSeries ts at different offsets determined by interval and horizon. The forecast data is materialized as a 2D array where each row is a forecast window.

Resolution¶

Infrastructure systems support two types of objects for passing the resolution: :class:datetime.timedelta and :class:dateutil.relativedelta.relativedelta. These types allow users to define durations with varying levels of granularity and semantic meaning. While timedelta is best suited for precise, fixed-length intervals (e.g., seconds, minutes, hours, days), relativedelta is more appropriate for calendar-aware durations such as months or years, which do not have a fixed number of days.

Internally, all durations, regardless of whether they are specified using timedelta or relativedelta, are normalized and serialized into a strict ISO 8601 format. This provides a consistent and standardized representation of durations across the system, ensuring compatibility and simplifying transport, storage, and validation. For example, a timedelta of 1 month will be converted to the ISO format string P1M and a timedelta of 1 hour will be converted to P0DT1H.

Behaviors¶

Users can customize time series behavior with these flags passed to the System constructor:

time_series_in_memory: The System stores each array of data in an Arrow file by default. This is a binary file that enables efficient storage and row access. Set this flag to store the data in memory instead.
time_series_read_only: The default behavior allows users to add and remove time series data. Set this flag to disable mutation. That can be useful if you are de-serializing a system, won’t be changing it, and want to avoid copying the data.
time_series_directory: The System stores time series data on the computer’s tmp filesystem by default. This filesystem may be of limited size. If your data will exceed that limit, such as what is likely to happen on an HPC compute node, set this parameter to an alternate location (such as /tmp/scratch on NREL’s HPC systems).

Refer to the Time Series API for more information.