diff --git a/pts/__init__.py b/pts/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pts/dataset/__init__.py b/pts/dataset/__init__.py
new file mode 100644
index 0000000..b995b7c
--- /dev/null
+++ b/pts/dataset/__init__.py
@@ -0,0 +1 @@
+from pts.dataset.list_dataset import ListDataset
diff --git a/pts/dataset/common.py b/pts/dataset/common.py
new file mode 100644
index 0000000..1cc464d
--- /dev/null
+++ b/pts/dataset/common.py
@@ -0,0 +1,21 @@
+from abc import ABC, abstractmethod
+
+from typing import Any, Dict, Sized, Iterable, NamedTuple
+
+
+DataEntry = Dict[str, Any]
+
+
+class SourceContext(NamedTuple):
+    source: str
+    row: int
+
+
+class Dataset(Sized, Iterable[DataEntry], ABC):
+    @abstractmethod
+    def __iter__(self) -> Iterable[DataEntry]:
+        pass
+
+    @abstractmethod
+    def __len__(self):
+        pass
diff --git a/pts/dataset/list_dataset.py b/pts/dataset/list_dataset.py
new file mode 100644
index 0000000..543a2d9
--- /dev/null
+++ b/pts/dataset/list_dataset.py
@@ -0,0 +1,21 @@
+from typing import Iterable
+
+from .common import Dataset, DataEntry, SourceContext
+from .process import ProcessDataEntry
+
+
+class ListDataset(Dataset):
+    def __init__(
+        self, data_iter: Iterable[DataEntry], freq: str, one_dim_target: bool = True
+    ) -> None:
+        process = ProcessDataEntry(freq, one_dim_target)
+        self.list_data = [process(data) for data in data_iter]
+
+    def __iter__(self):
+        source_name = "list_data"
+        for row_number, data in enumerate(self.list_data, start=1):
+            data['source'] = SourceContext(source=source_name, row=row_number)
+            yield data
+
+    def __len__(self):
+        return len(self.list_data)
diff --git a/pts/dataset/process.py b/pts/dataset/process.py
new file mode 100644
index 0000000..6f8a7a3
--- /dev/null
+++ b/pts/dataset/process.py
@@ -0,0 +1,101 @@
+from functools import lru_cache
+from typing import Callable, List, cast
+
+import numpy as np
+import pandas as pd
+from pandas.tseries.frequencies import to_offset
+
+from .common import DataEntry
+
+
+class ProcessStartField:
+    def __init__(self, name: str, freq: str) -> None:
+        self.name = name
+        self.freq = freq
+
+    def __call__(self, data: DataEntry) -> DataEntry:
+        try:
+            value = ProcessStartField.process(data[self.name], self.freq)
+        except (TypeError, ValueError) as e:
+            raise Exception(f'Error "{e}" occurred when reading field "{self.name}"')
+
+        data[self.name] = value
+
+        return data
+
+    @staticmethod
+    @lru_cache(maxsize=10000)
+    def process(string: str, freq: str) -> pd.Timestamp:
+        timestamp = pd.Timestamp(string, freq=freq)
+        # 'W-SUN' is the standardized freqstr for W
+        if timestamp.freq.name in ("M", "W-SUN"):
+            offset = to_offset(freq)
+            timestamp = timestamp.replace(
+                hour=0, minute=0, second=0, microsecond=0, nanosecond=0
+            )
+            return pd.Timestamp(offset.rollback(timestamp), freq=offset.freqstr)
+        if timestamp.freq == "B":
+            # does not floor on business day as it is not allowed
+            return timestamp
+        return pd.Timestamp(timestamp.floor(timestamp.freq), freq=timestamp.freq)
+
+
+class ProcessTimeSeriesField:
+    def __init__(self, name, is_required: bool, is_static: bool, is_cat: bool) -> None:
+        self.name = name
+        self.is_required = is_required
+        self.req_ndim = 1 if is_static else 2
+        self.dtype = np.int32 if is_cat else np.float32
+
+    def __call__(self, data: DataEntry) -> DataEntry:
+        value = data.get(self.name, None)
+
+        if value is not None:
+            value = np.asarray(value, dtype=self.dtype)
+            dim_diff = self.req_ndim - value.ndim
+            if dim_diff == 1:
+                value = np.expand_dims(a=value, axis=0)
+            elif dim_diff != 0:
+                raise Exception(
+                    f"JSON array has bad shape - expected {self.req_ndim} dimensions got {dim_diff}"
+                )
+
+            data[self.name] = value
+            return data
+        elif not self.is_required:
+            return data
+        else:
+            raise Exception(f"JSON object is missing a required field `{self.name}`")
+
+
+class ProcessDataEntry:
+    def __init__(self, freq: str, one_dim_target: bool = True) -> None:
+        self.trans = cast(
+            List[Callable[[DataEntry], DataEntry]],
+            [
+                ProcessStartField("start", freq=freq),
+                ProcessTimeSeriesField(
+                    "target", is_required=True, is_cat=False, is_static=one_dim_target
+                ),
+                ProcessTimeSeriesField(
+                    "feat_dynamic_cat", is_required=False, is_cat=True, is_static=False
+                ),
+                ProcessTimeSeriesField(
+                    "feat_dynamic_real",
+                    is_required=False,
+                    is_cat=False,
+                    is_static=False,
+                ),
+                ProcessTimeSeriesField(
+                    "feat_static_cat", is_required=False, is_cat=True, is_static=True
+                ),
+                ProcessTimeSeriesField(
+                    "feat_static_real", is_required=False, is_cat=False, is_static=True
+                ),
+            ],
+        )
+
+    def __call__(self, data: DataEntry) -> DataEntry:
+        for t in self.trans:
+            data = t(data)
+        return data