diff --git a/ocf_data_sampler/config/model.py b/ocf_data_sampler/config/model.py
index 0e13514..0ec5749 100644
--- a/ocf_data_sampler/config/model.py
+++ b/ocf_data_sampler/config/model.py
@@ -14,7 +14,8 @@
 from typing import Dict, List, Optional
 from typing_extensions import Self
 
-from pydantic import BaseModel, Field, RootModel, field_validator, model_validator
+from pydantic import BaseModel, Field, RootModel, field_validator, ValidationInfo, model_validator
+
 from ocf_data_sampler.constants import NWP_PROVIDERS
 
 logger = logging.getLogger(__name__)
@@ -40,6 +41,45 @@ class General(Base):
     )
 
 
+class TimeWindowMixin(Base):
+    """Mixin class, to add interval start, end and resolution minutes"""
+
+    time_resolution_minutes: int = Field(
+        ...,
+        gt=0,
+        description="The temporal resolution of the data in minutes",
+    )
+    
+    interval_start_minutes: int = Field(
+        ...,
+        description="Data interval starts at `t0 + interval_start_minutes`",
+    )
+
+    interval_end_minutes: int = Field(
+        ...,
+        description="Data interval ends at `t0 + interval_end_minutes`",
+    )
+    
+    @model_validator(mode='after')
+    def check_interval_range(cls, values):
+        if values.interval_start_minutes > values.interval_end_minutes:
+            raise ValueError('interval_start_minutes must be <= interval_end_minutes')
+        return values
+
+    @field_validator("interval_start_minutes")
+    def interval_start_minutes_divide_by_time_resolution(cls, v: int, info: ValidationInfo) -> int:
+        if v % info.data["time_resolution_minutes"] != 0:
+            raise ValueError("interval_start_minutes must be divisible by time_resolution_minutes")
+        return v
+
+    @field_validator("interval_end_minutes")
+    def interval_end_minutes_divide_by_time_resolution(cls, v: int, info: ValidationInfo) -> int:
+        if v % info.data["time_resolution_minutes"] != 0:
+            raise ValueError("interval_end_minutes must be divisible by time_resolution_minutes")
+        return v
+
+
+
 # noinspection PyMethodParameters
 class DropoutMixin(Base):
     """Mixin class, to add dropout minutes"""
@@ -76,54 +116,18 @@ def dropout_instructions_consistent(self) -> Self:
         return self
 
 
-# noinspection PyMethodParameters
-class TimeWindowMixin(Base):
-    """Time resolution mix in"""
-
-    time_resolution_minutes: int = Field(
-        ...,
-        gt=0,
-        description="The temporal resolution of the data in minutes",
-    )
-
-    forecast_minutes: int = Field(
-        ...,
-        ge=0,
-        description="how many minutes to forecast in the future",
-    )
-    history_minutes: int = Field(
-        ...,
-        ge=0,
-        description="how many historic minutes to use",
-    )
-
-    @field_validator("forecast_minutes")
-    def forecast_minutes_divide_by_time_resolution(cls, v, values) -> int:
-        if v % values.data["time_resolution_minutes"] != 0:
-            message = "Forecast duration must be divisible by time resolution"
-            logger.error(message)
-            raise Exception(message)
-        return v
-
-    @field_validator("history_minutes")
-    def history_minutes_divide_by_time_resolution(cls, v, values) -> int:
-        if v % values.data["time_resolution_minutes"] != 0:
-            message = "History duration must be divisible by time resolution"
-            logger.error(message)
-            raise Exception(message)
-        return v
-
-
 class SpatialWindowMixin(Base):
     """Mixin class, to add path and image size"""
 
     image_size_pixels_height: int = Field(
         ...,
+        ge=0,
         description="The number of pixels of the height of the region of interest",
     )
 
     image_size_pixels_width: int = Field(
         ...,
+        ge=0,
         description="The number of pixels of the width of the region of interest",
     )
 
@@ -140,10 +144,6 @@ class Satellite(TimeWindowMixin, DropoutMixin, SpatialWindowMixin):
         ..., description="the satellite channels that are used"
     )
 
-    live_delay_minutes: int = Field(
-        ..., description="The expected delay in minutes of the satellite data"
-    )
-
 
 # noinspection PyMethodParameters
 class NWP(TimeWindowMixin, DropoutMixin, SpatialWindowMixin):
@@ -169,6 +169,7 @@ class NWP(TimeWindowMixin, DropoutMixin, SpatialWindowMixin):
         " the maximum forecast horizon of the NWP and the requested forecast length.",
     )
 
+
     @field_validator("provider")
     def validate_provider(cls, v: str) -> str:
         """Validate 'provider'"""
@@ -227,11 +228,10 @@ class Site(TimeWindowMixin, DropoutMixin):
     # TODO validate the csv for metadata
 
 
+
 # noinspection PyPep8Naming
 class InputData(Base):
-    """
-    Input data model.
-    """
+    """Input data model"""
 
     satellite: Optional[Satellite] = None
     nwp: Optional[MultiNWP] = None
diff --git a/ocf_data_sampler/select/find_contiguous_time_periods.py b/ocf_data_sampler/select/find_contiguous_time_periods.py
index 9013513..28f5b3c 100644
--- a/ocf_data_sampler/select/find_contiguous_time_periods.py
+++ b/ocf_data_sampler/select/find_contiguous_time_periods.py
@@ -63,16 +63,16 @@ def find_contiguous_time_periods(
 
 def trim_contiguous_time_periods(
     contiguous_time_periods: pd.DataFrame, 
-    history_duration: pd.Timedelta,
-    forecast_duration: pd.Timedelta,
+    interval_start: pd.Timedelta,
+    interval_end: pd.Timedelta,
 ) -> pd.DataFrame:
     """Trim the contiguous time periods to allow for history and forecast durations.
 
     Args:
         contiguous_time_periods: DataFrame where each row represents a single time period. The 
             DataFrame must have `start_dt` and `end_dt` columns.
-        history_duration: Length of the historical slice used for a sample
-        forecast_duration: Length of the forecast slice used for a sample
+        interval_start: The start of the interval with respect to t0
+        interval_end: The end of the interval with respect to t0
 
 
     Returns:
@@ -80,8 +80,8 @@ def trim_contiguous_time_periods(
     """
     contiguous_time_periods = contiguous_time_periods.copy()
 
-    contiguous_time_periods["start_dt"] += history_duration
-    contiguous_time_periods["end_dt"] -= forecast_duration
+    contiguous_time_periods["start_dt"] -= interval_start
+    contiguous_time_periods["end_dt"] -= interval_end
 
     valid_mask = contiguous_time_periods["start_dt"] <= contiguous_time_periods["end_dt"]
     contiguous_time_periods = contiguous_time_periods.loc[valid_mask]
@@ -92,16 +92,16 @@ def trim_contiguous_time_periods(
 
 def find_contiguous_t0_periods(
         datetimes: pd.DatetimeIndex,
-        history_duration: pd.Timedelta,
-        forecast_duration: pd.Timedelta,
+        interval_start: pd.Timedelta,
+        interval_end: pd.Timedelta,
         sample_period_duration: pd.Timedelta,
     ) -> pd.DataFrame:
     """Return a pd.DataFrame where each row records the boundary of a contiguous time period.
 
     Args:
         datetimes: pd.DatetimeIndex. Must be sorted.
-        history_duration: Length of the historical slice used for each sample
-        forecast_duration: Length of the forecast slice used for each sample
+        interval_start: The start of the interval with respect to t0
+        interval_end: The end of the interval with respect to t0
         sample_period_duration: The sample frequency of the timeseries
 
 
@@ -109,7 +109,7 @@ def find_contiguous_t0_periods(
         pd.DataFrame where each row represents a single time period.  The pd.DataFrame
             has two columns: `start_dt` and `end_dt` (where 'dt' is short for 'datetime').
     """
-    total_duration = history_duration + forecast_duration
+    total_duration = interval_end - interval_start
     
     contiguous_time_periods = find_contiguous_time_periods(
         datetimes=datetimes,
@@ -119,8 +119,8 @@ def find_contiguous_t0_periods(
 
     contiguous_t0_periods = trim_contiguous_time_periods(
         contiguous_time_periods=contiguous_time_periods,
-        history_duration=history_duration,
-        forecast_duration=forecast_duration,
+        interval_start=interval_start,
+        interval_end=interval_end,
     )
 
     assert len(contiguous_t0_periods) > 0
@@ -128,92 +128,57 @@ def find_contiguous_t0_periods(
     return contiguous_t0_periods
 
 
-def _find_contiguous_t0_periods_nwp(
-        ds,
-        history_duration: pd.Timedelta,
-        forecast_duration: pd.Timedelta,
-        max_staleness: pd.Timedelta |  None = None,
-        max_dropout: pd.Timedelta = pd.Timedelta(0),
-        time_dim: str = "init_time_utc",
-        end_buffer: pd.Timedelta = pd.Timedelta(0),
-    ):
-
-    assert "step" in ds.coords
-    # It is possible to use up to this amount of max staleness for the dataset and slice
-    # required
-    possible_max_staleness = (
-        pd.Timedelta(ds["step"].max().item())
-        - forecast_duration
-        - end_buffer
-    )
-
-    # If max_staleness is set to None we set it based on the max step ahead of the input
-    # forecast data
-    if max_staleness is None:
-        max_staleness = possible_max_staleness
-    else:
-        # Make sure the max acceptable staleness isn't longer than the max possible
-        assert max_staleness <= possible_max_staleness
-        max_staleness = max_staleness
-
-    contiguous_time_periods = find_contiguous_t0_periods_nwp(
-        datetimes=pd.DatetimeIndex(ds[time_dim]),
-        history_duration=history_duration,
-        max_staleness=max_staleness,
-        max_dropout=max_dropout,
-    )
-    return contiguous_time_periods
-
-
-
 def find_contiguous_t0_periods_nwp(
-    datetimes: pd.DatetimeIndex,
-    history_duration: pd.Timedelta,
+    init_times: pd.DatetimeIndex,
+    interval_start: pd.Timedelta,
     max_staleness: pd.Timedelta,
     max_dropout: pd.Timedelta = pd.Timedelta(0),
+    first_forecast_step: pd.Timedelta = pd.Timedelta(0),
+    
 ) -> pd.DataFrame:
     """Get all time periods from the NWP init times which are valid as t0 datetimes.
 
     Args:
-        datetimes: Sorted pd.DatetimeIndex
-        history_duration: Length of the historical slice used for a sample
-        max_staleness: Up to how long after an NWP forecast init_time are we willing to use the
-            forecast. Each init time will only be used up to this t0 time regardless of the forecast
-            valid time.
+        init_times: The initialisation times of the available forecasts
+        interval_start: The start of the desired data interval with respect to t0
+        max_staleness: Up to how long after an init time are we willing to use the forecast. Each 
+            init time will only be used up to this t0 time regardless of the forecast valid time.
         max_dropout: What is the maximum amount of dropout that will be used. This must be <=
             max_staleness.
+        first_forecast_step: The timedelta of the first step of the forecast. By default we assume
+            the first valid time of the forecast is the same as its init time.
 
     Returns:
         pd.DataFrame where each row represents a single time period.  The pd.DataFrame
         has two columns: `start_dt` and `end_dt` (where 'dt' is short for 'datetime').
     """
     # Sanity checks.
-    assert len(datetimes) > 0
-    assert datetimes.is_monotonic_increasing
-    assert datetimes.is_unique
-    assert history_duration >= pd.Timedelta(0)
+    assert len(init_times) > 0
+    assert init_times.is_monotonic_increasing
+    assert init_times.is_unique
     assert max_staleness >= pd.Timedelta(0)
-    assert max_dropout <= max_staleness
+    assert pd.Timedelta(0) <= max_dropout <= max_staleness
 
-    hist_drop_buffer = max(history_duration, max_dropout)
+    hist_drop_buffer = max(first_forecast_step-interval_start, max_dropout)
 
     # Store contiguous periods
     contiguous_periods = []
 
-    # Start first period allowing for history slice and max dropout
-    start_this_period = datetimes[0] + hist_drop_buffer
+    # Begin the first period allowing for the time to the first_forecast_step, the length of the 
+    # interval sampled from before t0, and the dropout
+    start_this_period = init_times[0] + hist_drop_buffer
 
     # The first forecast is valid up to the max staleness
-    end_this_period = datetimes[0] + max_staleness
-
-    for dt_init in datetimes[1:]:
-        # If the previous init time becomes stale before the next init becomes valid whilst also
-        # considering dropout - then the contiguous period breaks, and new starts with considering
-        # dropout and history duration
-        if end_this_period < dt_init + max_dropout:
+    end_this_period = init_times[0] + max_staleness
+
+    for dt_init in init_times[1:]:
+        # If the previous init time becomes stale before the next init becomes valid (whilst also
+        # considering dropout) then the contiguous period breaks
+        # Else if the previous init time becomes stale before the fist step of the next forecast
+        # then this also causes a break in the contiguous period
+        if (end_this_period < dt_init + max(max_dropout, first_forecast_step)):
             contiguous_periods.append([start_this_period, end_this_period])
-
-            # And start a new period
+            # The new period begins with the same conditions as the first period
             start_this_period = dt_init + hist_drop_buffer
         end_this_period = dt_init + max_staleness
 
diff --git a/ocf_data_sampler/select/select_time_slice.py b/ocf_data_sampler/select/select_time_slice.py
index 65bbde2..8d5c31e 100644
--- a/ocf_data_sampler/select/select_time_slice.py
+++ b/ocf_data_sampler/select/select_time_slice.py
@@ -39,23 +39,14 @@ def _sel_fillinterp(
 def select_time_slice(
     ds: xr.DataArray,
     t0: pd.Timestamp,
+    interval_start: pd.Timedelta,
+    interval_end: pd.Timedelta,
     sample_period_duration: pd.Timedelta,
-    history_duration: pd.Timedelta | None = None,
-    forecast_duration: pd.Timedelta | None = None,
-    interval_start: pd.Timedelta | None = None,
-    interval_end: pd.Timedelta | None = None,
     fill_selection: bool = False,
     max_steps_gap: int = 0,
 ):
     """Select a time slice from a Dataset or DataArray."""
-    used_duration = history_duration is not None and forecast_duration is not None
-    used_intervals = interval_start is not None and interval_end is not None
-    assert used_duration ^ used_intervals, "Either durations, or intervals must be supplied"
     assert max_steps_gap >= 0, "max_steps_gap must be >= 0 "
-
-    if used_duration:
-        interval_start = - history_duration
-        interval_end = forecast_duration
     
     if fill_selection and max_steps_gap == 0:
         _sel = _sel_fillnan
@@ -75,11 +66,11 @@ def select_time_slice(
 
 
 def select_time_slice_nwp(
-    ds: xr.DataArray,
+    da: xr.DataArray,
     t0: pd.Timestamp,
+    interval_start: pd.Timedelta,
+    interval_end: pd.Timedelta,
     sample_period_duration: pd.Timedelta,
-    history_duration: pd.Timedelta,
-    forecast_duration: pd.Timedelta,
     dropout_timedeltas: list[pd.Timedelta] | None = None,
     dropout_frac: float | None = 0,
     accum_channels: list[str] = [],
@@ -92,31 +83,31 @@ def select_time_slice_nwp(
         ), "dropout timedeltas must be negative"
         assert len(dropout_timedeltas) >= 1
     assert 0 <= dropout_frac <= 1
-    _consider_dropout = (dropout_timedeltas is not None) and dropout_frac > 0
+    consider_dropout = (dropout_timedeltas is not None) and dropout_frac > 0
 
 
     # The accumatation and non-accumulation channels
     accum_channels = np.intersect1d(
-        ds[channel_dim_name].values, accum_channels
+        da[channel_dim_name].values, accum_channels
     )
     non_accum_channels = np.setdiff1d(
-        ds[channel_dim_name].values, accum_channels
+        da[channel_dim_name].values, accum_channels
     )
 
-    start_dt = (t0 - history_duration).ceil(sample_period_duration)
-    end_dt = (t0 + forecast_duration).ceil(sample_period_duration)
+    start_dt = (t0 + interval_start).ceil(sample_period_duration)
+    end_dt = (t0 + interval_end).ceil(sample_period_duration)
 
     target_times = pd.date_range(start_dt, end_dt, freq=sample_period_duration)
 
     # Maybe apply NWP dropout
-    if _consider_dropout and (np.random.uniform() < dropout_frac):
+    if consider_dropout and (np.random.uniform() < dropout_frac):
         dt = np.random.choice(dropout_timedeltas)
         t0_available = t0 + dt
     else:
         t0_available = t0
 
     # Forecasts made up to and including t0
-    available_init_times = ds.init_time_utc.sel(
+    available_init_times = da.init_time_utc.sel(
         init_time_utc=slice(None, t0_available)
     )
 
@@ -139,7 +130,7 @@ def select_time_slice_nwp(
     step_indexer = xr.DataArray(steps, coords=coords)
 
     if len(accum_channels) == 0:
-        xr_sel = ds.sel(step=step_indexer, init_time_utc=init_time_indexer)
+        da_sel = da.sel(step=step_indexer, init_time_utc=init_time_indexer)
 
     else:
         # First minimise the size of the dataset we are diffing
@@ -149,7 +140,7 @@ def select_time_slice_nwp(
         min_step = min(steps)
         max_step = max(steps) + sample_period_duration
 
-        xr_min = ds.sel(
+        da_min = da.sel(
             {
                 "init_time_utc": unique_init_times,
                 "step": slice(min_step, max_step),
@@ -157,28 +148,28 @@ def select_time_slice_nwp(
         )
 
         # Slice out the data which does not need to be diffed
-        xr_non_accum = xr_min.sel({channel_dim_name: non_accum_channels})
-        xr_sel_non_accum = xr_non_accum.sel(
+        da_non_accum = da_min.sel({channel_dim_name: non_accum_channels})
+        da_sel_non_accum = da_non_accum.sel(
             step=step_indexer, init_time_utc=init_time_indexer
         )
 
         # Slice out the channels which need to be diffed
-        xr_accum = xr_min.sel({channel_dim_name: accum_channels})
+        da_accum = da_min.sel({channel_dim_name: accum_channels})
 
         # Take the diff and slice requested data
-        xr_accum = xr_accum.diff(dim="step", label="lower")
-        xr_sel_accum = xr_accum.sel(step=step_indexer, init_time_utc=init_time_indexer)
+        da_accum = da_accum.diff(dim="step", label="lower")
+        da_sel_accum = da_accum.sel(step=step_indexer, init_time_utc=init_time_indexer)
 
         # Join diffed and non-diffed variables
-        xr_sel = xr.concat([xr_sel_non_accum, xr_sel_accum], dim=channel_dim_name)
+        da_sel = xr.concat([da_sel_non_accum, da_sel_accum], dim=channel_dim_name)
 
         # Reorder the variable back to the original order
-        xr_sel = xr_sel.sel({channel_dim_name: ds[channel_dim_name].values})
+        da_sel = da_sel.sel({channel_dim_name: da[channel_dim_name].values})
 
         # Rename the diffed channels
-        xr_sel[channel_dim_name] = [
+        da_sel[channel_dim_name] = [
             f"diff_{v}" if v in accum_channels else v
-            for v in xr_sel[channel_dim_name].values
+            for v in da_sel[channel_dim_name].values
         ]
 
-    return xr_sel
\ No newline at end of file
+    return da_sel
\ No newline at end of file
diff --git a/ocf_data_sampler/select/time_slice_for_dataset.py b/ocf_data_sampler/select/time_slice_for_dataset.py
index b7905fa..1247ae6 100644
--- a/ocf_data_sampler/select/time_slice_for_dataset.py
+++ b/ocf_data_sampler/select/time_slice_for_dataset.py
@@ -4,7 +4,7 @@
 from ocf_data_sampler.config import Configuration
 from ocf_data_sampler.select.dropout import draw_dropout_time, apply_dropout_time
 from ocf_data_sampler.select.select_time_slice import select_time_slice_nwp, select_time_slice
-from ocf_data_sampler.time_functions import minutes
+from ocf_data_sampler.utils import minutes
 
 
 def slice_datasets_by_time(
@@ -23,19 +23,19 @@ def slice_datasets_by_time(
     sliced_datasets_dict = {}
 
     if "nwp" in datasets_dict:
-
+        
         sliced_datasets_dict["nwp"] = {}
-
+        
         for nwp_key, da_nwp in datasets_dict["nwp"].items():
-
+                            
             nwp_config = config.input_data.nwp[nwp_key]
 
             sliced_datasets_dict["nwp"][nwp_key] = select_time_slice_nwp(
                 da_nwp,
                 t0,
                 sample_period_duration=minutes(nwp_config.time_resolution_minutes),
-                history_duration=minutes(nwp_config.history_minutes),
-                forecast_duration=minutes(nwp_config.forecast_minutes),
+                interval_start=minutes(nwp_config.interval_start_minutes),
+                interval_end=minutes(nwp_config.interval_end_minutes),
                 dropout_timedeltas=minutes(nwp_config.dropout_timedeltas_minutes),
                 dropout_frac=nwp_config.dropout_fraction,
                 accum_channels=nwp_config.accum_channels,
@@ -49,8 +49,8 @@ def slice_datasets_by_time(
             datasets_dict["sat"],
             t0,
             sample_period_duration=minutes(sat_config.time_resolution_minutes),
-            interval_start=minutes(-sat_config.history_minutes),
-            interval_end=minutes(-sat_config.live_delay_minutes),
+            interval_start=minutes(sat_config.interval_start_minutes),
+            interval_end=minutes(sat_config.interval_end_minutes),
             max_steps_gap=2,
         )
 
@@ -74,15 +74,15 @@ def slice_datasets_by_time(
             datasets_dict["gsp"],
             t0,
             sample_period_duration=minutes(gsp_config.time_resolution_minutes),
-            interval_start=minutes(30),
-            interval_end=minutes(gsp_config.forecast_minutes),
+            interval_start=minutes(gsp_config.time_resolution_minutes),
+            interval_end=minutes(gsp_config.interval_end_minutes),
         )
-
+    
         sliced_datasets_dict["gsp"] = select_time_slice(
             datasets_dict["gsp"],
             t0,
             sample_period_duration=minutes(gsp_config.time_resolution_minutes),
-            interval_start=-minutes(gsp_config.history_minutes),
+            interval_start=minutes(gsp_config.interval_start_minutes),
             interval_end=minutes(0),
         )
 
@@ -94,9 +94,10 @@ def slice_datasets_by_time(
         )
 
         sliced_datasets_dict["gsp"] = apply_dropout_time(
-            sliced_datasets_dict["gsp"], gsp_dropout_time
+            sliced_datasets_dict["gsp"], 
+            gsp_dropout_time
         )
-
+    
     if "site" in datasets_dict:
         site_config = config.input_data.site
 
@@ -104,8 +105,8 @@ def slice_datasets_by_time(
             datasets_dict["site"],
             t0,
             sample_period_duration=minutes(site_config.time_resolution_minutes),
-            interval_start=-minutes(site_config.history_minutes),
-            interval_end=minutes(site_config.forecast_minutes),
+            interval_start=minutes(site_config.interval_start_minutes),
+            interval_end=minutes(site_config.interval_end_minutes),
         )
 
         # Randomly sample dropout
diff --git a/ocf_data_sampler/torch_datasets/Readme.md b/ocf_data_sampler/torch_datasets/README.md
similarity index 96%
rename from ocf_data_sampler/torch_datasets/Readme.md
rename to ocf_data_sampler/torch_datasets/README.md
index 606de43..8abc2bb 100644
--- a/ocf_data_sampler/torch_datasets/Readme.md
+++ b/ocf_data_sampler/torch_datasets/README.md
@@ -4,7 +4,7 @@ The aim of this folder is to create torch datasets which can easily be used in o
 
 ## PVNet UK Regional
 
-This pipline is for creating GSP predictions which we have used in our PVNet model.
+This dataset is for creating GSP predictions which we have used in our PVNet model.
 
 ### Init
 
diff --git a/ocf_data_sampler/torch_datasets/process_and_combine.py b/ocf_data_sampler/torch_datasets/process_and_combine.py
index 334e0a6..a732ef0 100644
--- a/ocf_data_sampler/torch_datasets/process_and_combine.py
+++ b/ocf_data_sampler/torch_datasets/process_and_combine.py
@@ -15,7 +15,7 @@
 from ocf_data_sampler.numpy_batch.nwp import NWPBatchKey
 from ocf_data_sampler.select.geospatial import osgb_to_lon_lat
 from ocf_data_sampler.select.location import Location
-from ocf_data_sampler.time_functions import minutes
+from ocf_data_sampler.utils import minutes
 
 
 def process_and_combine_datasets(
@@ -23,7 +23,7 @@ def process_and_combine_datasets(
     config: Configuration,
     t0: pd.Timestamp,
     location: Location,
-    sun_position_key: str = 'gsp'
+    target_key: str = 'gsp'
 ) -> dict:
     """Normalize and convert data to numpy arrays"""
 
@@ -58,7 +58,8 @@ def process_and_combine_datasets(
 
         numpy_modalities.append(
             convert_gsp_to_numpy_batch(
-                da_gsp, t0_idx=gsp_config.history_minutes // gsp_config.time_resolution_minutes
+                da_gsp, 
+                t0_idx=-gsp_config.interval_start_minutes / gsp_config.time_resolution_minutes
             )
         )
 
@@ -80,34 +81,32 @@ def process_and_combine_datasets(
 
         numpy_modalities.append(
             convert_site_to_numpy_batch(
-                da_sites, t0_idx=site_config.history_minutes / site_config.time_resolution_minutes
+                da_sites, t0_idx=-site_config.interval_start_minutes / site_config.time_resolution_minutes
             )
         )
 
-    if sun_position_key == 'gsp':
+    if target_key == 'gsp':
         # Make sun coords NumpyBatch
         datetimes = pd.date_range(
-            t0 - minutes(gsp_config.history_minutes),
-            t0 + minutes(gsp_config.forecast_minutes),
+            t0+minutes(gsp_config.interval_start_minutes),
+            t0+minutes(gsp_config.interval_end_minutes),
             freq=minutes(gsp_config.time_resolution_minutes),
         )
 
         lon, lat = osgb_to_lon_lat(location.x, location.y)
-        key_prefix = "gsp"
 
-    elif sun_position_key == 'site':
+    elif target_key == 'site':
         # Make sun coords NumpyBatch
         datetimes = pd.date_range(
-            t0 - minutes(site_config.history_minutes),
-            t0 + minutes(site_config.forecast_minutes),
+            t0+minutes(site_config.interval_start_minutes),
+            t0+minutes(site_config.interval_end_minutes),
             freq=minutes(site_config.time_resolution_minutes),
         )
 
         lon, lat = location.x, location.y
-        key_prefix = "site"
 
     numpy_modalities.append(
-        make_sun_position_numpy_batch(datetimes, lon, lat, key_prefix=key_prefix)
+        make_sun_position_numpy_batch(datetimes, lon, lat, key_prefix=target_key)
     )
 
     # Combine all the modalities and fill NaNs
diff --git a/ocf_data_sampler/torch_datasets/pvnet_uk_regional.py b/ocf_data_sampler/torch_datasets/pvnet_uk_regional.py
index e9c1c42..1f6d435 100644
--- a/ocf_data_sampler/torch_datasets/pvnet_uk_regional.py
+++ b/ocf_data_sampler/torch_datasets/pvnet_uk_regional.py
@@ -9,7 +9,7 @@
 from ocf_data_sampler.config import Configuration, load_yaml_configuration
 from ocf_data_sampler.load.load_dataset import get_dataset_dict
 from ocf_data_sampler.select import fill_time_periods, Location, slice_datasets_by_space, slice_datasets_by_time
-from ocf_data_sampler.time_functions import minutes
+from ocf_data_sampler.utils import minutes
 from ocf_data_sampler.torch_datasets.process_and_combine import process_and_combine_datasets, compute
 from ocf_data_sampler.torch_datasets.valid_time_periods import find_valid_time_periods
 
diff --git a/ocf_data_sampler/torch_datasets/site.py b/ocf_data_sampler/torch_datasets/site.py
index b92f821..3ec2fc3 100644
--- a/ocf_data_sampler/torch_datasets/site.py
+++ b/ocf_data_sampler/torch_datasets/site.py
@@ -14,7 +14,7 @@
     intersection_of_multiple_dataframes_of_periods,
     slice_datasets_by_time, slice_datasets_by_space
 )
-from ocf_data_sampler.time_functions import minutes
+from ocf_data_sampler.utils import minutes
 from ocf_data_sampler.torch_datasets.process_and_combine import process_and_combine_datasets, compute
 from ocf_data_sampler.torch_datasets.valid_time_periods import find_valid_time_periods
 
@@ -22,8 +22,8 @@
 
 
 def find_valid_t0_and_site_ids(
-        datasets_dict: dict,
-        config: Configuration,
+    datasets_dict: dict,
+    config: Configuration,
 ) -> pd.DataFrame:
     """Find the t0 times where all of the requested input data is available
 
@@ -57,8 +57,8 @@ def find_valid_t0_and_site_ids(
         time_periods = find_contiguous_t0_periods(
             pd.DatetimeIndex(site["time_utc"]),
             sample_period_duration=minutes(site_config.time_resolution_minutes),
-            history_duration=minutes(site_config.history_minutes),
-            forecast_duration=minutes(site_config.forecast_minutes),
+            interval_start=minutes(site_config.interval_start_minutes),
+            interval_end=minutes(site_config.interval_end_minutes),
         )
         valid_time_periods_per_site = intersection_of_multiple_dataframes_of_periods(
             [valid_time_periods, time_periods]
@@ -100,10 +100,10 @@ def get_locations(site_xr: xr.Dataset):
 
 class SitesDataset(Dataset):
     def __init__(
-            self,
-            config_filename: str,
-            start_time: str | None = None,
-            end_time: str | None = None,
+        self,
+        config_filename: str,
+        start_time: str | None = None,
+        end_time: str | None = None,
     ):
         """A torch Dataset for creating PVNet Site samples
 
@@ -154,7 +154,7 @@ def _get_sample(self, t0: pd.Timestamp, location: Location) -> dict:
         sample_dict = slice_datasets_by_time(sample_dict, t0, self.config)
         sample_dict = compute(sample_dict)
 
-        sample = process_and_combine_datasets(sample_dict, self.config, t0, location, sun_position_key='site')
+        sample = process_and_combine_datasets(sample_dict, self.config, t0, location, target_key='site')
 
         return sample
 
diff --git a/ocf_data_sampler/torch_datasets/valid_time_periods.py b/ocf_data_sampler/torch_datasets/valid_time_periods.py
index 6c9b986..2212e6f 100644
--- a/ocf_data_sampler/torch_datasets/valid_time_periods.py
+++ b/ocf_data_sampler/torch_datasets/valid_time_periods.py
@@ -2,9 +2,13 @@
 import pandas as pd
 
 from ocf_data_sampler.config import Configuration
-from ocf_data_sampler.select.find_contiguous_time_periods import find_contiguous_t0_periods_nwp, \
-    find_contiguous_t0_periods, intersection_of_multiple_dataframes_of_periods
-from ocf_data_sampler.time_functions import minutes
+from ocf_data_sampler.select.find_contiguous_time_periods import (
+    find_contiguous_t0_periods_nwp,
+    find_contiguous_t0_periods, 
+    intersection_of_multiple_dataframes_of_periods,
+)
+from ocf_data_sampler.utils import minutes
+
 
 
 def find_valid_time_periods(
@@ -46,7 +50,7 @@ def find_valid_time_periods(
             # This is the max staleness we can use considering the max step of the input data
             max_possible_staleness = (
                 pd.Timedelta(da["step"].max().item())
-                - minutes(nwp_config.forecast_minutes)
+                - minutes(nwp_config.interval_end_minutes)
                 - end_buffer
             )
 
@@ -56,12 +60,16 @@ def find_valid_time_periods(
             else:
                 # Make sure the max acceptable staleness isn't longer than the max possible
                 assert max_staleness <= max_possible_staleness
+                
+            # Find the first forecast step
+            first_forecast_step = pd.Timedelta(da["step"].min().item())
 
             time_periods = find_contiguous_t0_periods_nwp(
-                datetimes=pd.DatetimeIndex(da["init_time_utc"]),
-                history_duration=minutes(nwp_config.history_minutes),
+                init_times=pd.DatetimeIndex(da["init_time_utc"]),
+                interval_start=minutes(nwp_config.interval_start_minutes),
                 max_staleness=max_staleness,
                 max_dropout=max_dropout,
+                first_forecast_step = first_forecast_step,
             )
 
             contiguous_time_periods[f'nwp_{nwp_key}'] = time_periods
@@ -72,8 +80,8 @@ def find_valid_time_periods(
         time_periods = find_contiguous_t0_periods(
             pd.DatetimeIndex(datasets_dict["sat"]["time_utc"]),
             sample_period_duration=minutes(sat_config.time_resolution_minutes),
-            history_duration=minutes(sat_config.history_minutes),
-            forecast_duration=minutes(sat_config.forecast_minutes),
+            interval_start=minutes(sat_config.interval_start_minutes),
+            interval_end=minutes(sat_config.interval_end_minutes),
         )
 
         contiguous_time_periods['sat'] = time_periods
@@ -84,8 +92,8 @@ def find_valid_time_periods(
         time_periods = find_contiguous_t0_periods(
             pd.DatetimeIndex(datasets_dict["gsp"]["time_utc"]),
             sample_period_duration=minutes(gsp_config.time_resolution_minutes),
-            history_duration=minutes(gsp_config.history_minutes),
-            forecast_duration=minutes(gsp_config.forecast_minutes),
+            interval_start=minutes(gsp_config.interval_start_minutes),
+            interval_end=minutes(gsp_config.interval_end_minutes),
         )
 
         contiguous_time_periods['gsp'] = time_periods
@@ -105,4 +113,4 @@ def find_valid_time_periods(
     if len(valid_time_periods) == 0:
         raise ValueError(f"No valid time periods found, {contiguous_time_periods=}")
 
-    return valid_time_periods
+    return valid_time_periods
\ No newline at end of file
diff --git a/ocf_data_sampler/time_functions.py b/ocf_data_sampler/utils.py
similarity index 71%
rename from ocf_data_sampler/time_functions.py
rename to ocf_data_sampler/utils.py
index bbb09fd..ed37cb2 100644
--- a/ocf_data_sampler/time_functions.py
+++ b/ocf_data_sampler/utils.py
@@ -7,5 +7,4 @@ def minutes(minutes: int | list[float]) -> pd.Timedelta | pd.TimedeltaIndex:
     Args:
         minutes: the number of minutes, single value or list
     """
-    minutes_delta = pd.to_timedelta(minutes, unit="m")
-    return minutes_delta
+    return pd.to_timedelta(minutes, unit="m")
diff --git a/tests/config/test_config.py b/tests/config/test_config.py
index 2a606d4..7cc9b09 100644
--- a/tests/config/test_config.py
+++ b/tests/config/test_config.py
@@ -68,27 +68,33 @@ def test_extra_field_error():
         _ = Configuration(**configuration_dict)
 
 
-def test_incorrect_forecast_minutes(test_config_filename):
+def test_incorrect_interval_start_minutes(test_config_filename):
     """
-    Check a forecast length not divisible by time resolution causes error
+    Check a history length not divisible by time resolution causes error
     """
 
     configuration = load_yaml_configuration(test_config_filename)
 
-    configuration.input_data.nwp['ukv'].forecast_minutes = 1111
-    with pytest.raises(Exception, match="duration must be divisible by time resolution"):
+    configuration.input_data.nwp['ukv'].interval_start_minutes = -1111
+    with pytest.raises(
+        ValueError, 
+        match="interval_start_minutes must be divisible by time_resolution_minutes"
+    ):
         _ = Configuration(**configuration.model_dump())
 
 
-def test_incorrect_history_minutes(test_config_filename):
+def test_incorrect_interval_end_minutes(test_config_filename):
     """
-    Check a history length not divisible by time resolution causes error
+    Check a forecast length not divisible by time resolution causes error
     """
 
     configuration = load_yaml_configuration(test_config_filename)
 
-    configuration.input_data.nwp['ukv'].history_minutes = 1111
-    with pytest.raises(Exception, match="duration must be divisible by time resolution"):
+    configuration.input_data.nwp['ukv'].interval_end_minutes = 1111
+    with pytest.raises(
+        ValueError, 
+        match="interval_end_minutes must be divisible by time_resolution_minutes"
+    ):
         _ = Configuration(**configuration.model_dump())
 
 
diff --git a/tests/conftest.py b/tests/conftest.py
index 2964d99..06e6348 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -250,11 +250,13 @@ def data_sites() -> Site:
         generation.to_netcdf(filename)
         meta_df.to_csv(filename_csv)
 
-        site = Site(file_path=filename,
-                    metadata_file_path=filename_csv,
-                    time_resolution_minutes=30,
-                    forecast_minutes=60,
-                    history_minutes=30)
+        site = Site(
+            file_path=filename,
+            metadata_file_path=filename_csv,
+            interval_start_minutes=-30,
+            interval_end_minutes=60,
+            time_resolution_minutes=30,
+        )
 
         yield site
 
diff --git a/tests/select/test_find_contiguous_time_periods.py b/tests/select/test_find_contiguous_time_periods.py
index 9455b82..5024318 100644
--- a/tests/select/test_find_contiguous_time_periods.py
+++ b/tests/select/test_find_contiguous_time_periods.py
@@ -11,8 +11,8 @@ def test_find_contiguous_t0_periods():
 
     # Create 5-minutely data timestamps
     freq = pd.Timedelta(5, "min")
-    history_duration = pd.Timedelta(60, "min")
-    forecast_duration = pd.Timedelta(15, "min")
+    interval_start = pd.Timedelta(-60, "min")
+    interval_end = pd.Timedelta(15, "min")
 
     datetimes = (
         pd.date_range("2023-01-01 12:00", "2023-01-01 17:00", freq=freq)
@@ -21,8 +21,8 @@ def test_find_contiguous_t0_periods():
 
     periods = find_contiguous_t0_periods(
         datetimes=datetimes,
-        history_duration=history_duration,
-        forecast_duration=forecast_duration,
+        interval_start=interval_start,
+        interval_end=interval_end,
         sample_period_duration=freq,
     )
 
@@ -135,7 +135,7 @@ def test_find_contiguous_t0_periods_nwp():
     # Create 3-hourly init times with a few time stamps missing
     freq = pd.Timedelta(3, "h")
 
-    datetimes = (
+    init_times = (
         pd.date_range("2023-01-01 03:00", "2023-01-02 21:00", freq=freq)
         .delete([1, 4, 5, 6, 7, 9, 10])
     )
@@ -146,13 +146,13 @@ def test_find_contiguous_t0_periods_nwp():
     max_dropouts_hr = [0, 0, 0, 0, 3]
 
     for i in range(len(expected_results)):
-        history_duration = pd.Timedelta(history_durations_hr[i], "h")
+        interval_start = pd.Timedelta(-history_durations_hr[i], "h")
         max_staleness = pd.Timedelta(max_stalenesses_hr[i], "h")
         max_dropout = pd.Timedelta(max_dropouts_hr[i], "h")
 
         time_periods = find_contiguous_t0_periods_nwp(
-            datetimes=datetimes,
-            history_duration=history_duration,
+            init_times=init_times,
+            interval_start=interval_start,
             max_staleness=max_staleness,
             max_dropout=max_dropout,
         )
diff --git a/tests/select/test_select_time_slice.py b/tests/select/test_select_time_slice.py
index dc01f9b..b4c8d9c 100644
--- a/tests/select/test_select_time_slice.py
+++ b/tests/select/test_select_time_slice.py
@@ -55,31 +55,19 @@ def test_select_time_slice(da_sat_like, t0_str):
 
     # Slice parameters
     t0 = pd.Timestamp(f"2024-01-02 {t0_str}")
-    forecast_duration = pd.Timedelta("0min")
-    history_duration = pd.Timedelta("60min")
+    interval_start = pd.Timedelta(-0, "min")
+    interval_end = pd.Timedelta(60, "min")
     freq = pd.Timedelta("5min")
 
     # Expect to return these timestamps from the selection
-    expected_datetimes = pd.date_range(t0 - history_duration, t0 + forecast_duration, freq=freq)
+    expected_datetimes = pd.date_range(t0 +interval_start, t0 + interval_end, freq=freq)
 
-    # Make the selection using the `[x]_duration` parameters
+    # Make the selection
     sat_sample = select_time_slice(
-        ds=da_sat_like,
+        da_sat_like,
         t0=t0,
-        history_duration=history_duration,
-        forecast_duration=forecast_duration,
-        sample_period_duration=freq,
-    )
-
-    # Check the returned times are as expected
-    assert (sat_sample.time_utc == expected_datetimes).all()
-
-    # Make the selection using the `interval_[x]` parameters
-    sat_sample = select_time_slice(
-        ds=da_sat_like,
-        t0=t0,
-        interval_start=-history_duration,
-        interval_end=forecast_duration,
+        interval_start=interval_start,
+        interval_end=interval_end,
         sample_period_duration=freq,
     )
 
@@ -93,8 +81,8 @@ def test_select_time_slice_out_of_bounds(da_sat_like, t0_str):
 
     # Slice parameters
     t0 = pd.Timestamp(f"2024-01-02 {t0_str}")
-    forecast_duration = pd.Timedelta("30min")
-    history_duration = pd.Timedelta("60min")
+    interval_start = pd.Timedelta(-30, "min")
+    interval_end = pd.Timedelta(60, "min")
     freq = pd.Timedelta("5min")
 
     # The data is available between these times
@@ -102,14 +90,14 @@ def test_select_time_slice_out_of_bounds(da_sat_like, t0_str):
     max_time = da_sat_like.time_utc.max()
 
     # Expect to return these timestamps from the selection
-    expected_datetimes = pd.date_range(t0 - history_duration, t0 + forecast_duration, freq=freq)
+    expected_datetimes = pd.date_range(t0 + interval_start, t0 + interval_end, freq=freq)
 
     # Make the partially out of bounds selection
     sat_sample = select_time_slice(
-        ds=da_sat_like,
+        da_sat_like,
         t0=t0,
-        history_duration=history_duration,
-        forecast_duration=forecast_duration,
+        interval_start=interval_start,
+        interval_end=interval_end,
         sample_period_duration=freq,
         fill_selection=True
     )
@@ -138,8 +126,8 @@ def test_select_time_slice_nwp_basic(da_nwp_like, t0_str):
 
     # Slice parameters
     t0 = pd.Timestamp(f"2024-01-02 {t0_str}")
-    forecast_duration = pd.Timedelta("6h")
-    history_duration = pd.Timedelta("3h")
+    interval_start = pd.Timedelta(-6, "h")
+    interval_end = pd.Timedelta(3, "h")
     freq = pd.Timedelta("1h")
 
     # Make the selection
@@ -147,8 +135,8 @@ def test_select_time_slice_nwp_basic(da_nwp_like, t0_str):
         da_nwp_like,
         t0,
         sample_period_duration=freq,
-        history_duration=history_duration,
-        forecast_duration=forecast_duration,
+        interval_start=interval_start,
+        interval_end=interval_end,
         dropout_timedeltas = None,
         dropout_frac = 0,
         accum_channels = [],
@@ -156,7 +144,7 @@ def test_select_time_slice_nwp_basic(da_nwp_like, t0_str):
     )
 
     # Check the target-times are as expected
-    expected_target_times = pd.date_range(t0 - history_duration, t0 + forecast_duration, freq=freq)
+    expected_target_times = pd.date_range(t0 + interval_start, t0 + interval_end, freq=freq)
     assert (da_slice.target_time_utc==expected_target_times).all()
 
     # Check the init-times are as expected
@@ -172,8 +160,8 @@ def test_select_time_slice_nwp_with_dropout(da_nwp_like, dropout_hours):
     """Test the functionality of select_time_slice_nwp with dropout"""
 
     t0 = pd.Timestamp("2024-01-02 12:00")
-    forecast_duration = pd.Timedelta("6h")
-    history_duration = pd.Timedelta("3h")
+    interval_start = pd.Timedelta(-6, "h")
+    interval_end = pd.Timedelta(3, "h")
     freq = pd.Timedelta("1h")
     dropout_timedelta = pd.Timedelta(f"-{dropout_hours}h")
 
@@ -181,8 +169,8 @@ def test_select_time_slice_nwp_with_dropout(da_nwp_like, dropout_hours):
         da_nwp_like,
         t0,
         sample_period_duration=freq,
-        history_duration=history_duration,
-        forecast_duration=forecast_duration,
+        interval_start=interval_start,
+        interval_end=interval_end,
         dropout_timedeltas = [dropout_timedelta],
         dropout_frac = 1,
         accum_channels = [],
@@ -190,7 +178,7 @@ def test_select_time_slice_nwp_with_dropout(da_nwp_like, dropout_hours):
     )
 
     # Check the target-times are as expected
-    expected_target_times = pd.date_range(t0 - history_duration, t0 + forecast_duration, freq=freq)
+    expected_target_times = pd.date_range(t0 + interval_start, t0 + interval_end, freq=freq)
     assert (da_slice.target_time_utc==expected_target_times).all()
 
     # Check the init-times are as expected considering the delay
@@ -207,9 +195,9 @@ def test_select_time_slice_nwp_with_dropout_and_accum(da_nwp_like, t0_str):
 
     # Slice parameters
     t0 = pd.Timestamp(f"2024-01-02 {t0_str}")
-    forecast_duration = pd.Timedelta("6h")
-    history_duration = pd.Timedelta("3h")
-    freq = pd.Timedelta("1h")
+    interval_start = pd.Timedelta(-6, "h")
+    interval_end = pd.Timedelta(3, "h")
+    freq = pd.Timedelta("1H")
     dropout_timedelta = pd.Timedelta("-2h")
 
     t0_delayed = (t0 + dropout_timedelta).floor(NWP_FREQ)
@@ -218,8 +206,8 @@ def test_select_time_slice_nwp_with_dropout_and_accum(da_nwp_like, t0_str):
         da_nwp_like,
         t0,
         sample_period_duration=freq,
-        history_duration=history_duration,
-        forecast_duration=forecast_duration,
+        interval_start=interval_start,
+        interval_end=interval_end,
         dropout_timedeltas=[dropout_timedelta],
         dropout_frac=1,
         accum_channels=["dswrf"],
@@ -227,7 +215,7 @@ def test_select_time_slice_nwp_with_dropout_and_accum(da_nwp_like, t0_str):
     )
 
     # Check the target-times are as expected
-    expected_target_times = pd.date_range(t0 - history_duration, t0 + forecast_duration, freq=freq)
+    expected_target_times = pd.date_range(t0 + interval_start, t0 + interval_end, freq=freq)
     assert (da_slice.target_time_utc==expected_target_times).all()
 
     # Check the init-times are as expected considering the delay
@@ -254,7 +242,7 @@ def test_select_time_slice_nwp_with_dropout_and_accum(da_nwp_like, t0_str):
             init_time_utc=t0_delayed, 
             channel="dswrf", 
         ).diff(dim="step", label="lower")
-        .sel(step=slice(t0-t0_delayed - history_duration, t0-t0_delayed + forecast_duration))
+        .sel(step=slice(t0-t0_delayed + interval_start, t0-t0_delayed + interval_end))
     )
 
     # Check the values are the same
@@ -275,7 +263,7 @@ def test_select_time_slice_nwp_with_dropout_and_accum(da_nwp_like, t0_str):
             init_time_utc=t0_delayed, 
             channel="t", 
         )
-        .sel(step=slice(t0-t0_delayed - history_duration, t0-t0_delayed + forecast_duration))
+        .sel(step=slice(t0-t0_delayed + interval_start, t0-t0_delayed + interval_end))
     )
 
     # Check the values are the same
diff --git a/tests/test_data/configs/pvnet_test_config.yaml b/tests/test_data/configs/pvnet_test_config.yaml
index f6c2125..01d29c2 100644
--- a/tests/test_data/configs/pvnet_test_config.yaml
+++ b/tests/test_data/configs/pvnet_test_config.yaml
@@ -6,8 +6,8 @@ input_data:
 
   gsp:
     zarr_path: set_in_temp_file
-    history_minutes: 60
-    forecast_minutes: 120
+    interval_start_minutes: -60
+    interval_end_minutes: 120
     time_resolution_minutes: 30
     dropout_timedeltas_minutes: null
     dropout_fraction: 0
@@ -16,8 +16,8 @@ input_data:
     ukv:
       provider: ukv
       zarr_path: set_in_temp_file
-      history_minutes: 60
-      forecast_minutes: 120
+      interval_start_minutes: -60
+      interval_end_minutes: 120
       time_resolution_minutes: 60
       channels:
         - t # 2-metre temperature
@@ -29,9 +29,8 @@ input_data:
 
   satellite:
     zarr_path: set_in_temp_file
-    history_minutes: 30
-    forecast_minutes: 0
-    live_delay_minutes: 0
+    interval_start_minutes: -30
+    interval_end_minutes: 0
     time_resolution_minutes: 5
     channels:
       - IR_016
diff --git a/tests/test_data/configs/test_config.yaml b/tests/test_data/configs/test_config.yaml
index 6b3ff6d..634fe73 100644
--- a/tests/test_data/configs/test_config.yaml
+++ b/tests/test_data/configs/test_config.yaml
@@ -1,35 +1,36 @@
 general:
   description: test example configuration
   name: example
+
 input_data:
   gsp:
     zarr_path: tests/data/gsp/test.zarr
-    history_minutes: 60
-    forecast_minutes: 120
+    interval_start_minutes: -60
+    interval_end_minutes: 120
     time_resolution_minutes: 30
     dropout_timedeltas_minutes: [-30]
     dropout_fraction: 0.1
   nwp:
     ukv:
+      zarr_path: tests/data/nwp_data/test.zarr
+      provider: "ukv"
+      interval_start_minutes: -60
+      interval_end_minutes: 120
+      time_resolution_minutes: 60
       channels:
         - t
       image_size_pixels_height: 2
       image_size_pixels_width: 2
-      zarr_path: tests/data/nwp_data/test.zarr
-      provider: "ukv"
-      history_minutes: 60
-      forecast_minutes: 120
-      time_resolution_minutes: 60
       dropout_timedeltas_minutes: [-180]
       dropout_fraction: 1.0
+      max_staleness_minutes: null
 
   satellite:
+    zarr_path: tests/data/sat_data.zarr
+    time_resolution_minutes: 15
+    interval_start_minutes: -60
+    interval_end_minutes: 0
     channels:
       - IR_016
     image_size_pixels_height: 24
     image_size_pixels_width: 24
-    zarr_path: tests/data/sat_data.zarr
-    time_resolution_minutes: 15
-    history_minutes: 60
-    forecast_minutes: 0
-    live_delay_minutes: 0
\ No newline at end of file