Add time series analysis page

app.py

@@ -18,6 +18,7 @@ def main() -> None:
         st.page_link("pages/data_explorer.py", label="Data Explorer", icon="📊")
         st.page_link("pages/modeling.py", label="Modeling", icon="🧠")
         st.page_link("pages/prediction.py", label="Prediction", icon="🔮")
+        st.page_link("pages/time_series.py", label="Time Series", icon="📈")
         st.page_link("pages/report.py", label="Report", icon="📄")
 
 

pages/data_explorer.py

@@ -49,6 +49,9 @@ def main() -> None:
                 st.session_state["data"] = data_utils.convert_dtypes(
                     st.session_state["data"]
                 )
+                st.session_state["datetime_cols"] = eda.detect_datetime_columns(
+                    st.session_state["data"]
+                )
                 st.success(f"{name} loaded!")
 
         with st.expander("Help"):
@@ -59,6 +62,7 @@ def main() -> None:
             df = data_utils.load_data(uploaded_file)
             df = data_utils.convert_dtypes(df)
             st.session_state["data"] = df
+            st.session_state["datetime_cols"] = eda.detect_datetime_columns(df)
             st.success("File loaded successfully!")
         except ValueError as exc:
             st.error(f"Failed to load file: {exc}")

pages/time_series.py

@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+from pathlib import Path
+import tempfile
+
+import pandas as pd
+import streamlit as st
+
+from utils import ui, viz
+
+st.set_page_config(page_title="Time Series", layout="wide")
+
+
+def main() -> None:
+    """Render the time series analysis page."""
+    ui.apply_branding()
+    st.title("Time Series Analysis")
+
+    df = st.session_state.get("data")
+    if df is None or df.empty:
+        st.info("No dataset available. Load data on the Data Explorer page.")
+        return
+
+    datetime_cols = st.session_state.get("datetime_cols") or []
+    if not datetime_cols:
+        st.info("No datetime columns detected in the dataset.")
+        return
+
+    with st.sidebar:
+        time_col = st.selectbox("Datetime Column", datetime_cols, key="ts_time")
+        numeric_cols = df.select_dtypes(include="number").columns.tolist()
+        value_col = st.selectbox("Value Column", numeric_cols, key="ts_value")
+        export_fmt = st.selectbox("Export Format", ["png", "jpg"], key="ts_fmt")
+        period = st.number_input(
+            "Seasonal Period", min_value=2, value=2, step=1, key="ts_period"
+        )
+
+    if st.button("Generate Plots"):
+        ts_fig = viz.time_series_plot(df, time_col, value_col, title="Time Series")
+        st.plotly_chart(ts_fig, use_container_width=True)
+        with tempfile.NamedTemporaryFile(suffix=f".{export_fmt}") as tmp:
+            viz.export_figure(ts_fig, Path(tmp.name))
+            tmp.seek(0)
+            st.download_button(
+                "Download Time Series",
+                data=tmp.read(),
+                file_name=f"time_series.{export_fmt}",
+                mime=f"image/{export_fmt}",
+            )
+
+        dec_fig = viz.decomposition_plot(
+            df.set_index(time_col)[value_col], period=period, title="Decomposition"
+        )
+        st.pyplot(dec_fig)
+        with tempfile.NamedTemporaryFile(suffix=f".{export_fmt}") as tmp:
+            viz.export_figure(dec_fig, Path(tmp.name))
+            tmp.seek(0)
+            st.download_button(
+                "Download Decomposition",
+                data=tmp.read(),
+                file_name=f"decomposition.{export_fmt}",
+                mime=f"image/{export_fmt}",
+            )
+
+
+if __name__ == "__main__":
+    main()

tests/test_pages.py

@@ -11,6 +11,7 @@
     "pages.modeling",
     "pages.prediction",
     "pages.report",
+    "pages.time_series",
 ]
 
 @pytest.mark.parametrize("mod_name", PAGES)
@@ -65,3 +66,27 @@ def test_modeling_page_widgets_exist():
     assert "Train Models" in content
     assert "export_model" in content
 
+
+def test_time_series_page_runs(monkeypatch):
+    import streamlit as st
+    from pages import time_series
+
+    df = pd.DataFrame(
+        {
+            "date": pd.date_range("2021-01-01", periods=5, freq="D"),
+            "value": range(5),
+        }
+    )
+    st.session_state.clear()
+    st.session_state["data"] = df
+    st.session_state["datetime_cols"] = ["date"]
+    monkeypatch.setattr(time_series.ui, "apply_branding", lambda: None)
+    time_series.main()
+
+
+def test_time_series_page_contents():
+    with open("pages/time_series.py", "r", encoding="utf-8") as f:
+        content = f.read()
+    assert "time_series_plot" in content
+    assert "decomposition_plot" in content
+