diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8af6dcf67..dadb8fa0b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -16,6 +16,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Add `TSDataset.features` property to get list of all features in a dataset ([#405](https://github.com/etna-team/etna/pull/405))
 - Add `MADOutlierTransform` class for anomaly detection ([#415](https://github.com/etna-team/etna/pull/415))
 - Add `MeanEncoderTransform` ([#413](https://github.com/etna-team/etna/pull/413))
+- Add `FourierDecomposeTransform` transform for series decomposition using DFT ([#430](https://github.com/etna-team/etna/pull/430))
 
 ### Changed
 - Allow to change `device`, `batch_size` and `num_workers` of embedding models ([#396](https://github.com/etna-team/etna/pull/396))
@@ -35,6 +36,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fix typo in 103 tutorial ([#408](https://github.com/etna-team/etna/pull/408))
 - Remove sorting of `ts.df` by timestamps in `plot_forecast` and `plot_forecast_decomposition` ([#410](https://github.com/etna-team/etna/pull/410))
 - Fix forecast visualization with `horizon=1` ([#426](https://github.com/etna-team/etna/pull/426))
+- Set upper bound `<2` on numpy version ([#431](https://github.com/etna-team/etna/pull/431))
+- 
 - 
 - Fix `VotingEnsemble`, `StackingEnsemble`, `DirectEnsemble` have a valid `params_to_tune` that returns empty dict ([#432](https://github.com/etna-team/etna/pull/432))
 - Fix passing custom model to `STLTransform` ([#412](https://github.com/etna-team/etna/pull/412))
diff --git a/docs/source/api_reference/transforms.rst b/docs/source/api_reference/transforms.rst
index 42fca2c0f..55a146690 100644
--- a/docs/source/api_reference/transforms.rst
+++ b/docs/source/api_reference/transforms.rst
@@ -44,6 +44,7 @@ Decomposition transforms and their utilities:
    decomposition.MedianPerIntervalModel
    decomposition.SklearnPreprocessingPerIntervalModel
    decomposition.SklearnRegressionPerIntervalModel
+   decomposition.FourierDecomposeTransform
 
 Categorical encoding transforms:
 
diff --git a/etna/experimental/change_points/regularization_search.py b/etna/experimental/change_points/regularization_search.py
index 62dbd5c3f..c3faa5093 100644
--- a/etna/experimental/change_points/regularization_search.py
+++ b/etna/experimental/change_points/regularization_search.py
@@ -71,9 +71,9 @@ def _get_next_value(
         next value and its bounds
     """
     if need_greater:
-        return np.mean([now_value, lower_bound]), lower_bound, now_value
+        return float(np.mean([now_value, lower_bound])), lower_bound, now_value
     else:
-        return np.mean([now_value, upper_bound]), now_value, upper_bound
+        return float(np.mean([now_value, upper_bound])), now_value, upper_bound
 
 
 def bin_search(
@@ -121,7 +121,7 @@ def bin_search(
         raise ValueError("Impossible number of changepoints. Please, increase max_value or increase n_bkps value.")
 
     lower_bound, upper_bound = 0.0, max_value
-    now_value = np.mean([lower_bound, upper_bound])
+    now_value = float(np.mean([lower_bound, upper_bound]))
     now_n_bkps = _get_n_bkps(series, change_point_model, **{opt_param: now_value})
     iters = 0
 
diff --git a/etna/transforms/__init__.py b/etna/transforms/__init__.py
index fb4b93896..44c536bac 100644
--- a/etna/transforms/__init__.py
+++ b/etna/transforms/__init__.py
@@ -12,6 +12,7 @@
 from etna.transforms.decomposition import ChangePointsSegmentationTransform
 from etna.transforms.decomposition import ChangePointsTrendTransform
 from etna.transforms.decomposition import DeseasonalityTransform
+from etna.transforms.decomposition import FourierDecomposeTransform
 from etna.transforms.decomposition import IrreversibleChangePointsTransform
 from etna.transforms.decomposition import LinearTrendTransform
 from etna.transforms.decomposition import ReversibleChangePointsTransform
diff --git a/etna/transforms/decomposition/__init__.py b/etna/transforms/decomposition/__init__.py
index a5516ec62..ae2558af6 100644
--- a/etna/transforms/decomposition/__init__.py
+++ b/etna/transforms/decomposition/__init__.py
@@ -16,4 +16,5 @@
 from etna.transforms.decomposition.deseasonal import DeseasonalityTransform
 from etna.transforms.decomposition.detrend import LinearTrendTransform
 from etna.transforms.decomposition.detrend import TheilSenTrendTransform
+from etna.transforms.decomposition.dft_based import FourierDecomposeTransform
 from etna.transforms.decomposition.stl import STLTransform
diff --git a/etna/transforms/decomposition/dft_based.py b/etna/transforms/decomposition/dft_based.py
new file mode 100644
index 000000000..def3eeed8
--- /dev/null
+++ b/etna/transforms/decomposition/dft_based.py
@@ -0,0 +1,200 @@
+from typing import List
+
+import numpy as np
+import pandas as pd
+
+from etna.datasets import TSDataset
+from etna.datasets.utils import determine_num_steps
+from etna.transforms import IrreversibleTransform
+
+
+class FourierDecomposeTransform(IrreversibleTransform):
+    """Transform that uses Fourier transformation to estimate series decomposition.
+
+    Note
+    ----
+    This transform decomposes only in-sample data. For the future timestamps it produces ``NaN``.
+    For the dataset to be transformed, it should contain at least the minimum amount of in-sample timestamps that are required by transform.
+
+    Warning
+    -------
+    This transform adds new columns to the dataset, that correspond to the selected frequencies. Such columns are named with
+    ``dft_{i}`` suffix. Suffix index do NOT indicate any relation to the frequencies. Produced names should be thought of as
+    arbitrary identifiers to the produced sinusoids.
+    """
+
+    def __init__(self, k: int, in_column: str = "target", residuals: bool = False):
+        """Init ``FourierDecomposeTransform``.
+
+        Parameters
+        ----------
+        k:
+            how many top positive frequencies selected for the decomposition. Selection performed proportional to the amplitudes.
+        in_column:
+            name of the processed column.
+        residuals:
+            whether to add residuals after decomposition. This guarantees that all components, including residuals, sum up to the series.
+        """
+        if k <= 0:
+            raise ValueError("Parameter `k` must be positive integer!")
+
+        self.k = k
+        self.in_column = in_column
+        self.residuals = residuals
+
+        self._first_timestamp = None
+        self._last_timestamp = None
+
+        super().__init__(required_features=[in_column])
+
+    def get_regressors_info(self) -> List[str]:
+        """Return the list with regressors created by the transform."""
+        return []
+
+    def _fit(self, df: pd.DataFrame):
+        """Fit transform with the dataframe."""
+        pass
+
+    def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Transform provided dataframe."""
+        pass
+
+    @staticmethod
+    def _get_num_pos_freqs(series: pd.Series) -> int:
+        """Get number of positive frequencies for the series."""
+        num_obs = len(series)
+        return int(np.ceil((num_obs - 1) / 2) + 1)
+
+    def _check_segments(self, df: pd.DataFrame):
+        """Check if series satisfy conditions."""
+        segments_with_missing = []
+        min_num_pos_freq = float("inf")
+        for segment in df:
+            series = df[segment]
+            series = series.loc[series.first_valid_index() : series.last_valid_index()]
+            if series.isna().any():
+                segments_with_missing.append(segment)
+
+            min_num_pos_freq = min(min_num_pos_freq, self._get_num_pos_freqs(series))
+
+        if len(segments_with_missing) > 0:
+            raise ValueError(
+                f"Feature `{self.in_column}` contains missing values in segments: {segments_with_missing}!"
+            )
+
+        if self.k > min_num_pos_freq:
+            raise ValueError(f"Parameter `k` must not be greater then {min_num_pos_freq} for the provided dataset!")
+
+    def _dft_components(self, series: pd.Series) -> pd.DataFrame:
+        """Estimate series decomposition using FFT."""
+        initial_index = series.index
+        series = series.loc[series.first_valid_index() : series.last_valid_index()]
+
+        num_pos_freqs = self._get_num_pos_freqs(series)
+
+        # compute Fourier decomposition of the series
+        dft_series = np.fft.fft(series)
+
+        # compute "amplitudes" for each frequency
+        abs_dft_series = np.abs(dft_series)
+
+        # select top-k indices
+        abs_pos_dft_series = abs_dft_series[:num_pos_freqs]
+        top_k_idxs = np.argpartition(abs_pos_dft_series, num_pos_freqs - self.k)[-self.k :]
+
+        # select top-k and separate each frequency
+        freq_matrix = np.diag(dft_series)
+        freq_matrix = freq_matrix[:num_pos_freqs]
+        selected_freqs = freq_matrix[top_k_idxs]
+
+        # return frequencies to initial domain
+        components = np.fft.ifft(selected_freqs).real
+
+        components_df = pd.DataFrame(
+            data=components.T, columns=[f"dft_{i}" for i in range(components.shape[0])], index=series.index
+        )
+
+        if self.residuals:
+            components_df["dft_residuals"] = series.values - np.sum(components, axis=0)
+
+        # return trailing and leading nans to the series if any existed initially
+        if not components_df.index.equals(initial_index):
+            components_df = components_df.reindex(index=initial_index, fill_value=np.nan)
+
+        return components_df
+
+    def fit(self, ts: TSDataset) -> "FourierDecomposeTransform":
+        """Fit the transform and the decomposition model.
+
+        Parameters
+        ----------
+        ts:
+            dataset to fit the transform on.
+
+        Returns
+        -------
+        :
+            the fitted transform instance.
+        """
+        self._first_timestamp = ts.index.min()
+        self._last_timestamp = ts.index.max()
+
+        self._check_segments(df=ts[..., self.in_column].droplevel("feature", axis=1))
+
+        return self
+
+    def transform(self, ts: TSDataset) -> TSDataset:
+        """Transform ``TSDataset`` inplace.
+
+        Parameters
+        ----------
+        ts:
+            Dataset to transform.
+
+        Returns
+        -------
+        :
+            Transformed ``TSDataset``.
+        """
+        if self._first_timestamp is None:
+            raise ValueError("Transform is not fitted!")
+
+        if ts.index.min() < self._first_timestamp:
+            raise ValueError(
+                f"First index of the dataset to be transformed must be larger or equal than {self._first_timestamp}!"
+            )
+
+        if ts.index.min() > self._last_timestamp:
+            raise ValueError(
+                f"Dataset to be transformed must contain historical observations in range {self._first_timestamp} - {self._last_timestamp}"
+            )
+
+        segment_df = ts[..., self.in_column].droplevel("feature", axis=1)
+
+        ts_max_timestamp = ts.index.max()
+        if ts_max_timestamp > self._last_timestamp:
+            future_steps = determine_num_steps(self._last_timestamp, ts_max_timestamp, freq=ts.freq)
+            segment_df.iloc[-future_steps:] = np.nan
+
+        self._check_segments(df=segment_df)
+
+        segments = segment_df.columns
+        segment_components = []
+        for segment in segments:
+            components_df = self._dft_components(series=segment_df[segment])
+            components_df.columns = f"{self.in_column}_" + components_df.columns
+
+            components_df.columns = pd.MultiIndex.from_product(
+                [[segment], components_df.columns], names=["segment", "feature"]
+            )
+
+            segment_components.append(components_df)
+
+        segment_components = pd.concat(segment_components, axis=1)
+
+        ts.add_columns_from_pandas(segment_components)
+
+        return ts
+
+
+__all__ = ["FourierDecomposeTransform"]
diff --git a/poetry.lock b/poetry.lock
index 8b4dfd39a..bd2ee60ef 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1741,7 +1741,7 @@ files = [
 name = "importlib-metadata"
 version = "6.6.0"
 description = "Read metadata from Python packages"
-optional = true
+optional = false
 python-versions = ">=3.7"
 files = [
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@@ -2306,39 +2306,35 @@ typing = ["mypy (>=1.0.0)"]
 
 [[package]]
 name = "llvmlite"
-version = "0.38.1"
+version = "0.41.1"
 description = "lightweight wrapper around basic LLVM functionality"
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+    {file = "numpy-1.24.4-cp311-cp311-win_amd64.whl", hash = "sha256:b7b1fc9864d7d39e28f41d089bfd6353cb5f27ecd9905348c24187a768c79694"},
+    {file = "numpy-1.24.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1452241c290f3e2a312c137a9999cdbf63f78864d63c79039bda65ee86943f61"},
+    {file = "numpy-1.24.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:04640dab83f7c6c85abf9cd729c5b65f1ebd0ccf9de90b270cd61935eef0197f"},
+    {file = "numpy-1.24.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5425b114831d1e77e4b5d812b69d11d962e104095a5b9c3b641a218abcc050e"},
+    {file = "numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd80e219fd4c71fc3699fc1dadac5dcf4fd882bfc6f7ec53d30fa197b8ee22dc"},
+    {file = "numpy-1.24.4-cp38-cp38-win32.whl", hash = "sha256:4602244f345453db537be5314d3983dbf5834a9701b7723ec28923e2889e0bb2"},
+    {file = "numpy-1.24.4-cp38-cp38-win_amd64.whl", hash = "sha256:692f2e0f55794943c5bfff12b3f56f99af76f902fc47487bdfe97856de51a706"},
+    {file = "numpy-1.24.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:2541312fbf09977f3b3ad449c4e5f4bb55d0dbf79226d7724211acc905049400"},
+    {file = "numpy-1.24.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9667575fb6d13c95f1b36aca12c5ee3356bf001b714fc354eb5465ce1609e62f"},
+    {file = "numpy-1.24.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f3a86ed21e4f87050382c7bc96571755193c4c1392490744ac73d660e8f564a9"},
+    {file = "numpy-1.24.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d11efb4dbecbdf22508d55e48d9c8384db795e1b7b51ea735289ff96613ff74d"},
+    {file = "numpy-1.24.4-cp39-cp39-win32.whl", hash = "sha256:6620c0acd41dbcb368610bb2f4d83145674040025e5536954782467100aa8835"},
+    {file = "numpy-1.24.4-cp39-cp39-win_amd64.whl", hash = "sha256:befe2bf740fd8373cf56149a5c23a0f601e82869598d41f8e188a0e9869926f8"},
+    {file = "numpy-1.24.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:31f13e25b4e304632a4619d0e0777662c2ffea99fcae2029556b17d8ff958aef"},
+    {file = "numpy-1.24.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95f7ac6540e95bc440ad77f56e520da5bf877f87dca58bd095288dce8940532a"},
+    {file = "numpy-1.24.4-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:e98f220aa76ca2a977fe435f5b04d7b3470c0a2e6312907b37ba6068f26787f2"},
+    {file = "numpy-1.24.4.tar.gz", hash = "sha256:80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463"},
 ]
 
 [[package]]
@@ -5856,4 +5851,4 @@ wandb = ["wandb"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.0, <3.11.0"
-content-hash = "69c6c274909c072aa5efc1a8bd519dae2eecf68b34c242a284bd48d615a04efe"
+content-hash = "b743c4a186808f1b1ccde63212570c9f8b925ba214c799fe8887c514071edb36"
diff --git a/pyproject.toml b/pyproject.toml
index 2d2e48231..938989dc5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -61,7 +61,7 @@ boto3 = "^1.5"
 botocore = "*"
 scipy = "^1.0"
 Bottleneck = "^1.3.4"
-numpy = "*"
+numpy = "<2"
 joblib = "*"
 plotly = "*"
 hydra_slayer = "*"
diff --git a/tests/conftest.py b/tests/conftest.py
index dd81e2e3c..260afbef2 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -908,3 +908,29 @@ def ts_with_binary_exog() -> TSDataset:
     df_exog = TSDataset.to_dataset(df_exog)
     ts = TSDataset(df, freq="D", df_exog=df_exog, known_future="all")
     return ts
+
+
+@pytest.fixture()
+def outliers_solid_tsds():
+    """Create TSDataset with outliers and same last date."""
+    timestamp = pd.date_range("2021-01-01", end="2021-02-20", freq="D")
+    target1 = [np.sin(i) for i in range(len(timestamp))]
+    target1[10] += 10
+
+    target2 = [np.sin(i) for i in range(len(timestamp))]
+    target2[8] += 8
+    target2[15] = 2
+    target2[26] -= 12
+
+    df1 = pd.DataFrame({"timestamp": timestamp, "target": target1, "segment": "1"})
+    df2 = pd.DataFrame({"timestamp": timestamp, "target": target2, "segment": "2"})
+    df = pd.concat([df1, df2], ignore_index=True)
+    df_exog = df.copy()
+    df_exog.columns = ["timestamp", "regressor_1", "segment"]
+    ts = TSDataset(
+        df=TSDataset.to_dataset(df).iloc[:-10],
+        df_exog=TSDataset.to_dataset(df_exog),
+        freq="D",
+        known_future="all",
+    )
+    return ts
diff --git a/tests/test_analysis/test_eda/test_plots.py b/tests/test_analysis/test_eda/test_plots.py
index 6f1cd9ad0..d38a688e6 100644
--- a/tests/test_analysis/test_eda/test_plots.py
+++ b/tests/test_analysis/test_eda/test_plots.py
@@ -95,6 +95,7 @@ def test_cross_corr_normed(a, b, expected_result):
     np.testing.assert_almost_equal(result, expected_result)
 
 
+@pytest.mark.filterwarnings("ignore: invalid value encountered in scalar divide")
 @pytest.mark.parametrize(
     "a, b, normed, expected_result",
     [
diff --git a/tests/test_transforms/test_decomposition/test_change_points_based/test_segmentation.py b/tests/test_transforms/test_decomposition/test_change_points_based/test_segmentation.py
index 4f1045c46..eaa6fd76e 100644
--- a/tests/test_transforms/test_decomposition/test_change_points_based/test_segmentation.py
+++ b/tests/test_transforms/test_decomposition/test_change_points_based/test_segmentation.py
@@ -66,6 +66,8 @@ def test_transform_format_one_segment(pre_transformed_df: pd.DataFrame):
     assert transformed[OUT_COLUMN].dtype == "category"
 
 
+# issue with assert on pandas==1.2 and pandas==1.3
+@pytest.mark.filterwarnings("ignore: invalid value encountered in cast")
 def test_monotonously_result(pre_transformed_df: pd.DataFrame):
     """Check that resulting column is monotonously non-decreasing."""
     change_points_model = RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=N_BKPS)
@@ -100,6 +102,8 @@ def test_backtest(simple_ar_ts):
     _, _, _ = pipeline.backtest(ts=simple_ar_ts, metrics=[SMAPE()], n_folds=3)
 
 
+# issue with assert on pandas==1.2 and pandas==1.3
+@pytest.mark.filterwarnings("ignore: invalid value encountered in cast")
 def test_future_and_past_filling(simple_ar_ts):
     change_points_model = RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=N_BKPS)
     bs = ChangePointsSegmentationTransform(
@@ -115,6 +119,8 @@ def test_future_and_past_filling(simple_ar_ts):
         assert (after.to_pandas()[seg][OUT_COLUMN].astype(int) == 5).all()
 
 
+# issue with assert on pandas==1.2 and pandas==1.3
+@pytest.mark.filterwarnings("ignore: invalid value encountered in cast")
 def test_make_future(simple_ar_ts):
     change_points_model = RupturesChangePointsModel(change_points_model=Binseg(), n_bkps=N_BKPS)
     bs = ChangePointsSegmentationTransform(
diff --git a/tests/test_transforms/test_decomposition/test_dft_based.py b/tests/test_transforms/test_decomposition/test_dft_based.py
new file mode 100644
index 000000000..e773d02b5
--- /dev/null
+++ b/tests/test_transforms/test_decomposition/test_dft_based.py
@@ -0,0 +1,343 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from etna.datasets import TSDataset
+from etna.datasets import generate_ar_df
+from etna.metrics import MAE
+from etna.models import CatBoostPerSegmentModel
+from etna.models import HoltWintersModel
+from etna.models import ProphetModel
+from etna.pipeline import Pipeline
+from etna.transforms import FourierDecomposeTransform
+from etna.transforms import IForestOutlierTransform
+from etna.transforms import TimeSeriesImputerTransform
+
+
+def simple_pipeline_with_decompose(in_column, horizon, k):
+    pipeline = Pipeline(
+        transforms=[FourierDecomposeTransform(k=k, in_column=in_column)],
+        model=HoltWintersModel(),
+        horizon=horizon,
+    )
+    return pipeline
+
+
+@pytest.fixture()
+def ts_with_exogs() -> TSDataset:
+    periods = 100
+    periods_exog = periods + 10
+    df = generate_ar_df(start_time="2020-01-01", periods=periods, freq="D", n_segments=2)
+    df_exog = generate_ar_df(start_time="2020-01-01", periods=periods_exog, freq="D", n_segments=2, random_seed=2)
+    df_exog.rename(columns={"target": "exog"}, inplace=True)
+    df_exog["holiday"] = np.random.choice([0, 1], size=periods_exog * 2)
+
+    ts = TSDataset(df, freq="D", df_exog=df_exog, known_future="all")
+    return ts
+
+
+@pytest.fixture()
+def ts_with_exogs_train_test(ts_with_exogs):
+    return ts_with_exogs.train_test_split(test_size=20)
+
+
+@pytest.fixture()
+def forward_stride_datasets(ts_with_exogs):
+    train_df = ts_with_exogs.df.iloc[:-10]
+    test_df = ts_with_exogs.df.iloc[-20:]
+
+    train_ts = TSDataset(df=train_df, freq=ts_with_exogs.freq)
+    test_ts = TSDataset(df=test_df, freq=ts_with_exogs.freq)
+
+    return train_ts, test_ts
+
+
+@pytest.fixture()
+def ts_with_missing(ts_with_exogs):
+    target_df = ts_with_exogs[..., "target"]
+    target_df.iloc[10] = np.nan
+
+    return TSDataset(df=target_df, freq=ts_with_exogs.freq)
+
+
+@pytest.mark.parametrize("in_column", ("target", "feat"))
+def test_init(in_column):
+    transform = FourierDecomposeTransform(k=5, in_column=in_column)
+    assert transform.required_features == [in_column]
+    assert transform._first_timestamp is None
+    assert transform._last_timestamp is None
+
+
+@pytest.mark.parametrize("k", (-1, 0))
+def test_invalid_k(k):
+    with pytest.raises(ValueError, match="Parameter `k` must be positive integer!"):
+        FourierDecomposeTransform(k=k, in_column="target")
+
+
+@pytest.mark.parametrize(
+    "series, answ",
+    (
+        (pd.Series([1]), 1),
+        (pd.Series([1, 2]), 2),
+        (pd.Series([1, 2, 3]), 2),
+        (pd.Series([1, 2, 3, 4]), 3),
+        (pd.Series([1, 2, 3, 4, 5]), 3),
+        (pd.Series([1, 2, 3, 4, 5, 6]), 4),
+    ),
+)
+def test_get_num_pos_freqs(series, answ):
+    res = FourierDecomposeTransform._get_num_pos_freqs(series=series)
+    assert res == answ
+
+
+def test_check_segments_missing_values(ts_with_missing):
+    df = ts_with_missing[..., "target"]
+    transform = FourierDecomposeTransform(k=5)
+    with pytest.raises(ValueError, match=f"Feature `target` contains missing values"):
+        transform._check_segments(df=df)
+
+
+@pytest.mark.parametrize("k", (52, 100))
+def test_check_segments_large_k(ts_with_exogs, k):
+    df = ts_with_exogs[..., "target"]
+    transform = FourierDecomposeTransform(k=k)
+    with pytest.raises(ValueError, match=f"Parameter `k` must not be greater then"):
+        transform._check_segments(df=df)
+
+
+def test_check_segments_ok(ts_with_exogs):
+    df = ts_with_exogs[..., "target"]
+    transform = FourierDecomposeTransform(k=5)
+    transform._check_segments(df=df)
+
+
+@pytest.mark.parametrize(
+    "series",
+    (
+        pd.Series(np.arange(5)),
+        pd.Series(np.arange(10)),
+        pd.Series([np.nan] * 2 + list(range(5)) + [np.nan] * 3),
+    ),
+)
+def test_fft_components_out_format(series):
+    expected_columns = ["dft_0", "dft_1", "dft_2", "dft_residuals"]
+    transform = FourierDecomposeTransform(k=3, residuals=True)
+
+    decompose_df = transform._dft_components(series=series)
+
+    assert isinstance(decompose_df, pd.DataFrame)
+    pd.testing.assert_index_equal(decompose_df.index, series.index)
+    assert (decompose_df.columns == expected_columns).all()
+    np.testing.assert_allclose(np.sum(decompose_df.values, axis=1), series.values)
+
+
+def test_is_not_fitted(simple_tsdf):
+    transform = FourierDecomposeTransform(k=5, in_column="feat")
+    with pytest.raises(ValueError, match="Transform is not fitted!"):
+        transform.transform(ts=simple_tsdf)
+
+
+@pytest.mark.parametrize(
+    "ts_name,in_column",
+    (
+        ("outliers_df_with_two_columns", "target"),
+        ("outliers_df_with_two_columns", "feature"),
+        ("ts_with_exogs", "target"),
+        ("ts_with_exogs", "exog"),
+        ("ts_with_exogs", "holiday"),
+        ("example_tsds_int_timestamp", "target"),
+    ),
+)
+def test_fit(ts_name, in_column, request):
+    ts = request.getfixturevalue(ts_name)
+    transform = FourierDecomposeTransform(k=5, in_column=in_column)
+    transform.fit(ts=ts)
+
+    assert transform._first_timestamp == ts.index.min()
+    assert transform._last_timestamp == ts.index.max()
+
+
+@pytest.mark.parametrize("residuals", (True, False))
+@pytest.mark.parametrize("in_column", ("target", "exog"))
+def test_add_residuals(ts_with_exogs, residuals, in_column):
+    ts = ts_with_exogs
+
+    transform = FourierDecomposeTransform(k=5, in_column=in_column, residuals=residuals)
+    transformed = transform.fit_transform(ts=ts)
+
+    assert (f"{in_column}_dft_residuals" in transformed.features) is residuals
+
+
+def test_timestamp_from_history(ts_with_exogs_train_test):
+    test, train = ts_with_exogs_train_test
+    transform = FourierDecomposeTransform(k=5)
+    transform.fit_transform(train)
+
+    with pytest.raises(ValueError, match="First index of the dataset to be transformed must be larger"):
+        transform.transform(test)
+
+
+def test_timestamp_from_future(ts_with_exogs_train_test):
+    train, test = ts_with_exogs_train_test
+    transform = FourierDecomposeTransform(k=5)
+    transform.fit_transform(train)
+
+    with pytest.raises(ValueError, match="Dataset to be transformed must contain historical observations in range"):
+        transform.transform(test)
+
+
+@pytest.mark.parametrize(
+    "in_column",
+    (
+        "target",
+        "holiday",
+        "exog",
+    ),
+)
+@pytest.mark.parametrize("horizon", (1, 5))
+def test_simple_pipeline_forecast(ts_with_exogs, in_column, horizon):
+    ts = ts_with_exogs
+
+    pipeline = simple_pipeline_with_decompose(in_column=in_column, horizon=horizon, k=5)
+
+    pipeline.fit(ts=ts)
+    forecast = pipeline.forecast()
+
+    assert forecast.size()[0] == horizon
+    assert np.sum(forecast[..., "target"].isna().sum()) == 0
+
+
+@pytest.mark.parametrize(
+    "in_column",
+    (
+        "target",
+        "holiday",
+        "exog",
+    ),
+)
+@pytest.mark.parametrize("horizon", (1, 5))
+def test_simple_pipeline_predict(ts_with_exogs, in_column, horizon):
+    ts = ts_with_exogs
+
+    pipeline = simple_pipeline_with_decompose(in_column=in_column, horizon=horizon, k=5)
+
+    pipeline.fit(ts=ts)
+    forecast = pipeline.predict(ts)
+
+    assert forecast.size()[0] == ts.size()[0]
+    assert np.sum(forecast[..., "target"].isna().sum()) == 0
+
+
+@pytest.mark.parametrize(
+    "in_column",
+    (
+        "target",
+        "holiday",
+        "exog",
+    ),
+)
+@pytest.mark.parametrize("horizon", (1, 5))
+def test_simple_pipeline_predict_components(ts_with_exogs, in_column, horizon):
+    ts = ts_with_exogs
+
+    pipeline = simple_pipeline_with_decompose(in_column=in_column, horizon=horizon, k=5)
+
+    pipeline.fit(ts=ts)
+    forecast = pipeline.predict(ts, return_components=True)
+
+    assert forecast.size()[0] == ts.size()[0]
+    assert forecast.target_components_names == ("target_component_level",)
+
+
+@pytest.mark.parametrize(
+    "in_column",
+    (
+        "target",
+        "holiday",
+        "exog",
+    ),
+)
+@pytest.mark.parametrize("horizon", (1, 5))
+def test_simple_pipeline_backtest(ts_with_exogs, in_column, horizon):
+    ts = ts_with_exogs
+
+    pipeline = simple_pipeline_with_decompose(in_column=in_column, horizon=horizon, k=5)
+
+    _, forecast, _ = pipeline.backtest(ts=ts, metrics=[MAE()], n_folds=3)
+
+    assert len(forecast) == horizon * 3
+    assert np.sum(forecast.loc[:, pd.IndexSlice[:, "target"]].isna().sum()) == 0
+
+
+@pytest.mark.parametrize(
+    "ts_name,in_column",
+    (
+        ("outliers_df_with_two_columns", "target"),
+        ("outliers_df_with_two_columns", "feature"),
+        ("ts_with_exogs", "target"),
+        ("ts_with_exogs", "exog"),
+    ),
+)
+@pytest.mark.parametrize("k", (1, 5, 10, 40, 51))
+@pytest.mark.parametrize("forecast_model", (ProphetModel(), CatBoostPerSegmentModel(iterations=10)))
+def test_pipeline_parameter_k(ts_name, in_column, forecast_model, k, request):
+    ts = request.getfixturevalue(ts_name)
+
+    pipeline = Pipeline(
+        transforms=[FourierDecomposeTransform(k=5, in_column=in_column)],
+        model=forecast_model,
+        horizon=3,
+    )
+
+    pipeline.fit(ts)
+    forecast = pipeline.forecast()
+
+    assert forecast.size()[0] == 3
+    assert np.sum(forecast.loc[:, pd.IndexSlice[:, "target"]].isna().sum()) == 0
+
+
+@pytest.mark.parametrize("answer", ({"1": ["2021-01-11"], "2": ["2021-01-09"]},))
+def test_outlier_detection(outliers_solid_tsds, answer):
+    ts = outliers_solid_tsds
+
+    transforms = [
+        FourierDecomposeTransform(k=2, in_column="target", residuals=True),
+        IForestOutlierTransform(
+            in_column="target",
+            features_to_ignore=["target", "regressor_1"],
+            contamination=0.01,
+        ),
+    ]
+    ts.fit_transform(transforms)
+
+    for segment in ts.segments:
+        empty_values = pd.isna(ts[:, segment, "target"])
+        assert empty_values.sum() == len(answer[segment])
+        assert all(empty_values[answer[segment]])
+
+
+def test_outlier_detection_pipeline(outliers_solid_tsds):
+    ts = outliers_solid_tsds
+    pipeline = Pipeline(
+        transforms=[
+            FourierDecomposeTransform(k=5, in_column="target"),
+            IForestOutlierTransform(in_column="target"),
+            TimeSeriesImputerTransform(in_column="target"),
+        ],
+        model=ProphetModel(),
+        horizon=3,
+    )
+    pipeline.fit(ts)
+
+
+@pytest.mark.parametrize("k", (1, 5))
+def test_stride_transform(forward_stride_datasets, k):
+    train, test = forward_stride_datasets
+
+    transform = FourierDecomposeTransform(k=k, residuals=True)
+
+    transform.fit(train)
+    transformed = transform.transform(test)
+
+    assert not transformed.df.iloc[:10].isna().any().any()
+    assert transformed.df.iloc[10:].isna().all().any()
diff --git a/tests/test_transforms/test_inference/test_inverse_transform.py b/tests/test_transforms/test_inference/test_inverse_transform.py
index 34f43fcc2..63f8aa2bc 100644
--- a/tests/test_transforms/test_inference/test_inverse_transform.py
+++ b/tests/test_transforms/test_inference/test_inverse_transform.py
@@ -23,6 +23,7 @@
 from etna.transforms import EventTransform
 from etna.transforms import ExogShiftTransform
 from etna.transforms import FilterFeaturesTransform
+from etna.transforms import FourierDecomposeTransform
 from etna.transforms import FourierTransform
 from etna.transforms import GaleShapleyFeatureSelectionTransform
 from etna.transforms import HolidayTransform
@@ -142,6 +143,7 @@ def _test_inverse_transform_train(self, ts, transform, expected_changes):
                 "regular_ts",
                 {},
             ),
+            (FourierDecomposeTransform(in_column="target", k=5, residuals=True), "regular_ts", {}),
             # embeddings
             (
                 EmbeddingSegmentTransform(
@@ -604,6 +606,7 @@ def test_inverse_transform_train_fail_resample(self, transform, dataset_name, ex
                 "regular_ts",
                 {},
             ),
+            (FourierDecomposeTransform(in_column="target", k=5, residuals=True), "regular_ts", {}),
             # embeddings
             (
                 EmbeddingSegmentTransform(
@@ -1096,6 +1099,7 @@ def _test_inverse_transform_train_subset_segments(self, ts, transform, segments)
                 ),
                 "regular_ts",
             ),
+            (FourierDecomposeTransform(in_column="target", k=5, residuals=True), "regular_ts"),
             # embeddings
             (
                 EmbeddingSegmentTransform(
@@ -1382,6 +1386,8 @@ def _test_inverse_transform_future_subset_segments(self, ts, transform, segments
                 ),
                 "regular_ts",
             ),
+            (FourierDecomposeTransform(in_column="target", k=5, residuals=True), "regular_ts"),
+            (FourierDecomposeTransform(in_column="positive", k=5, residuals=True), "ts_with_exog"),
             # embeddings
             (
                 EmbeddingSegmentTransform(
@@ -2996,6 +3002,19 @@ def test_inverse_transform_future_with_target_fail_difference(
         with pytest.raises(ValueError, match="Test should go after the train without gaps"):
             self._test_inverse_transform_future_with_target(ts, transform, expected_changes=expected_changes)
 
+    @pytest.mark.parametrize(
+        "transform, dataset_name, expected_changes",
+        [
+            (FourierDecomposeTransform(in_column="target", k=5, residuals=True), "regular_ts", {}),
+        ],
+    )
+    def test_inverse_transform_future_with_target_fail_require_history(
+        self, transform, dataset_name, expected_changes, request
+    ):
+        ts = request.getfixturevalue(dataset_name)
+        with pytest.raises(ValueError, match="Dataset to be transformed must contain historical observations"):
+            self._test_inverse_transform_future_with_target(ts, transform, expected_changes=expected_changes)
+
     # It is the only transform that doesn't change values back during `inverse_transform`
     @to_be_fixed(raises=AssertionError)
     @pytest.mark.parametrize(
@@ -3106,6 +3125,8 @@ def _test_inverse_transform_future_without_target(
                 "regular_ts",
                 {},
             ),
+            (FourierDecomposeTransform(in_column="target", k=5, residuals=True), "regular_ts", {}),
+            (FourierDecomposeTransform(in_column="positive", k=5, residuals=True), "ts_with_exog", {}),
             # embeddings
             (
                 EmbeddingSegmentTransform(
diff --git a/tests/test_transforms/test_inference/test_transform.py b/tests/test_transforms/test_inference/test_transform.py
index 3f1073a73..9d6cc7866 100644
--- a/tests/test_transforms/test_inference/test_transform.py
+++ b/tests/test_transforms/test_inference/test_transform.py
@@ -23,6 +23,7 @@
 from etna.transforms import EventTransform
 from etna.transforms import ExogShiftTransform
 from etna.transforms import FilterFeaturesTransform
+from etna.transforms import FourierDecomposeTransform
 from etna.transforms import FourierTransform
 from etna.transforms import GaleShapleyFeatureSelectionTransform
 from etna.transforms import HolidayTransform
@@ -128,6 +129,11 @@ def _test_transform_train(self, ts, transform, expected_changes):
                 "regular_ts",
                 {"create": {"res"}},
             ),
+            (
+                FourierDecomposeTransform(in_column="target", k=2, residuals=True),
+                "regular_ts",
+                {"create": {"target_dft_0", "target_dft_1", "target_dft_residuals"}},
+            ),
             # embeddings
             (
                 EmbeddingSegmentTransform(
@@ -550,6 +556,11 @@ def test_transform_train_datetime_timestamp(self, transform, dataset_name, expec
                 "regular_ts",
                 {"create": {"res"}},
             ),
+            (
+                FourierDecomposeTransform(in_column="target", k=2, residuals=True),
+                "regular_ts",
+                {"create": {"target_dft_0", "target_dft_1", "target_dft_residuals"}},
+            ),
             # embeddings
             (
                 EmbeddingSegmentTransform(
@@ -1060,6 +1071,7 @@ def _test_transform_train_subset_segments(self, ts, transform, segments):
                 ),
                 "regular_ts",
             ),
+            (FourierDecomposeTransform(in_column="target", k=2, residuals=True), "regular_ts"),
             # embeddings
             (
                 EmbeddingSegmentTransform(
@@ -1328,6 +1340,8 @@ def _test_transform_future_subset_segments(self, ts, transform, segments, horizo
                 ),
                 "regular_ts",
             ),
+            (FourierDecomposeTransform(in_column="target", k=2, residuals=True), "regular_ts"),
+            (FourierDecomposeTransform(in_column="positive", k=2, residuals=True), "ts_with_exog"),
             # embeddings
             (
                 EmbeddingSegmentTransform(
@@ -2816,6 +2830,23 @@ def test_transform_future_with_target(self, transform, dataset_name, expected_ch
         ts = request.getfixturevalue(dataset_name)
         self._test_transform_future_with_target(ts, transform, expected_changes=expected_changes)
 
+    @pytest.mark.parametrize(
+        "transform, dataset_name, expected_changes",
+        (
+            (
+                FourierDecomposeTransform(in_column="target", k=2, residuals=True),
+                "regular_ts",
+                {"create": {"target_dft_0", "target_dft_1", "target_dft_residuals"}},
+            ),
+        ),
+    )
+    def test_transform_future_with_target_fail_require_history(
+        self, transform, dataset_name, expected_changes, request
+    ):
+        ts = request.getfixturevalue(dataset_name)
+        with pytest.raises(ValueError, match="Dataset to be transformed must contain historical observations"):
+            self._test_transform_future_with_target(ts, transform, expected_changes=expected_changes)
+
 
 class TestTransformFutureWithoutTarget:
     """Test transform on future dataset with unknown target.
@@ -2889,6 +2920,16 @@ def _test_transform_future_without_target(self, ts, transform, expected_changes,
                 "regular_ts",
                 {"create": {"res"}},
             ),
+            (
+                FourierDecomposeTransform(in_column="target", k=2, residuals=True),
+                "regular_ts",
+                {"create": {"target_dft_0", "target_dft_1", "target_dft_residuals"}},
+            ),
+            (
+                FourierDecomposeTransform(in_column="positive", k=2, residuals=True),
+                "ts_with_exog",
+                {"create": {"positive_dft_0", "positive_dft_1", "positive_dft_residuals"}},
+            ),
             # embeddings
             (
                 EmbeddingSegmentTransform(
diff --git a/tests/test_transforms/test_outliers/test_outliers_transform.py b/tests/test_transforms/test_outliers/test_outliers_transform.py
index 87ff27ecb..daad6fc8d 100644
--- a/tests/test_transforms/test_outliers/test_outliers_transform.py
+++ b/tests/test_transforms/test_outliers/test_outliers_transform.py
@@ -78,32 +78,6 @@ def compare_outputs(ts, in_column, method, transform_constructor, method_kwargs,
         assert np.all(transformed_column[transformed_column.isna()].index == nan_timestamps)
 
 
-@pytest.fixture()
-def outliers_solid_tsds():
-    """Create TSDataset with outliers and same last date."""
-    timestamp = pd.date_range("2021-01-01", end="2021-02-20", freq="D")
-    target1 = [np.sin(i) for i in range(len(timestamp))]
-    target1[10] += 10
-
-    target2 = [np.sin(i) for i in range(len(timestamp))]
-    target2[8] += 8
-    target2[15] = 2
-    target2[26] -= 12
-
-    df1 = pd.DataFrame({"timestamp": timestamp, "target": target1, "segment": "1"})
-    df2 = pd.DataFrame({"timestamp": timestamp, "target": target2, "segment": "2"})
-    df = pd.concat([df1, df2], ignore_index=True)
-    df_exog = df.copy()
-    df_exog.columns = ["timestamp", "regressor_1", "segment"]
-    ts = TSDataset(
-        df=TSDataset.to_dataset(df).iloc[:-10],
-        df_exog=TSDataset.to_dataset(df_exog),
-        freq="D",
-        known_future="all",
-    )
-    return ts
-
-
 @pytest.fixture()
 def outliers_solid_tsds_with_holidays(outliers_solid_tsds):
     """Create TSDataset with outliers with holidays"""