fix typos

docs/source/guides/time_series.ipynb

@@ -98,7 +98,7 @@
     "\n",
     "## Gap and Window Length\n",
     "\n",
-    "Note that we will be using integers when defining the gap an window length. This implies that our data occurs at evenly spaced intervals--in this case daily--so a number `n` corresponds to `n` days. Support for unevenly spaced intervals is ongoing and can be explored with the Woodwork method [df.ww.infer_temporal_frequencies](https://woodwork.alteryx.com/en/stable/generated/woodwork.table_accessor.WoodworkTableAccessor.infer_temporal_frequencies.html#woodwork.table_accessor.WoodworkTableAccessor.infer_temporal_frequencies).\n",
+    "Note that we will be using integers when defining the gap and window length. This implies that our data occurs at evenly spaced intervals--in this case daily--so a number `n` corresponds to `n` days. Support for unevenly spaced intervals is ongoing and can be explored with the Woodwork method [df.ww.infer_temporal_frequencies](https://woodwork.alteryx.com/en/stable/generated/woodwork.table_accessor.WoodworkTableAccessor.infer_temporal_frequencies.html#woodwork.table_accessor.WoodworkTableAccessor.infer_temporal_frequencies).\n",
     "\n",
     "If we are at a point in time `t`, we have access to information from times less than `t` (past values), and we do not have information from times greater than `t` (future values). Our limitations in feature engineering, then, will come from when exactly before `t` we have access to the data. \n",
     "\n",
@@ -177,7 +177,7 @@
    "source": [
     "### Rolling Transform Primitives\n",
     "\n",
-    "Since we have access to the entire feature engineering window, we can aggregate over that window. Featuretools has several rolling primitives with which we can achieve this. Here, we'll use the `RollingMean` primitives `RollingMin`, setting the `gap` and `window_length` accordingly. Here, the gap is incredibly important, because when the gap is zero, it means the current observation's taret value is present in the window, which exposes our target.\n",
+    "Since we have access to the entire feature engineering window, we can aggregate over that window. Featuretools has several rolling primitives with which we can achieve this. Here, we'll use the `RollingMean` and `RollingMin` primitives, setting the `gap` and `window_length` accordingly. Here, the gap is incredibly important, because when the gap is zero, it means the current observation's taret value is present in the window, which exposes our target.\n",
     "\n",
     "This concern also exists for other primitives that reference earlier values in the dataframe. Because of this, when using primitives for time series feature engineering, one must be incredibly careful to not use primitives on the target column that incorporate the current observation when calculating a feature value."
    ]