Time-series forecasting (Exponential Smoothing) with Darts Integration (#1851)

* Working exponential with bugs * utilizing business datatime + fillers * working figures * expo with more functionality * historical forcasting feat. * numeric values for forcasted days * print pretty predictions * formatting * code cleanup * doc strings fix * more docs * quick fix * refactoring * poetry updates * typo * test script update * update requirements.txt * refactoring and Hugo website * Update _index.md * reverting poetry and requirements for now. * minor mod and removal of space * Update readme for forecasting * removal of old TF version * reqs and poetry * linting * merge main.yml from main * supress warnings due to lib version change * reverting main.yml * retrying expo on main.yml * fixing fundamentalanalysis version * update poetry fundamentalanalysis * type casting arg parse * seasonal period default update * update en.yml Co-authored-by: didierlopes.eth <dro.lopes@campus.fct.unl.pt> Co-authored-by: James Maslek <jmaslek11@gmail.com>
2026-11-15 05:41:49 +00:00 · 2022-05-24 17:33:56 -04:00
parent 707fd8f506
commit 5f8eb43620
16 changed files with 687 additions and 14 deletions
--- a/i18n/en.yml
+++ b/i18n/en.yml
@@ -486,6 +486,7 @@ en:
  stocks/pred/lstm: Long-Short Term Memory
  stocks/pred/conv1d: 1D Convolutional Neural Network
  stocks/pred/mc: Monte-Carlo simulations
+  stocks/pred/expo: Probabilistic Exponential Smoothing
  crypto/load: load a specific cryptocurrency for analysis
  crypto/find: find coins
  crypto/_coin: Coin
--- a/openbb_terminal/README.md
+++ b/openbb_terminal/README.md
@@ -186,17 +186,41 @@ Several features in this project utilize Machine Learning. Machine Learning Pyth

   To enable the `prediction` menu install additional dependencies after installing main dependencies:

-   - On M1 mac
+   - On Intel mac

     ```bash
     conda install -c conda-forge tensorflow==2.7.0
+     conda install -c conda-forge -c pytorch u8darts-torch
     poetry install -E prediction
     ```

+   - On M1 mac (make sure you are using miniconda3 or miniforge3)
+
+     ```bash
+     brew install cmake
+     brew install gcc
+     conda install -c apple tensorflow-deps
+     conda install -c apple tensorflow-deps==2.8.0
+     python -m pip install tensorflow-metal
+     conda update pip
+     conda update -c defaults numpy
+     export GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=1
+     export GRPC_PYTHON_BUILD_SYSTEM_ZLIB=1
+     wget https://raw.githubusercontent.com/Homebrew/homebrew-core/fb8323f2b170bd4ae97e1bac9bf3e2983af3fdb0/Formula/libomp.rb
+     brew unlink libomp
+     brew install libomp.rb
+     pip3 install -r requirements.txt
+     conda install -c conda-forge tensorflow==2.8.0
+     conda install -c conda-forge -c pytorch u8darts-torch
+     python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
+     ```
+
   - On all other systems

     ```bash
     poetry install -E prediction
+     conda install -c conda-forge -c pytorch u8darts-torch
+
     ```

   If you are having trouble with Poetry (e.g. on a Windows system), simply install requirements.txt with pip
--- a/openbb_terminal/common/prediction_techniques/ets_view.py
+++ b/openbb_terminal/common/prediction_techniques/ets_view.py
@@ -43,7 +43,7 @@ def display_exponential_smoothing(
    n_predict: int,
    trend: str = "N",
    seasonal: str = "N",
-    seasonal_periods: int = 5,
+    seasonal_periods: int = 7,
    s_end_date: str = "",
    export: str = "",
    time_res: str = "",
--- a/openbb_terminal/common/prediction_techniques/expo_model.py
+++ b/openbb_terminal/common/prediction_techniques/expo_model.py
@@ -0,0 +1,137 @@
+"""Probabilistic Exponential Smoothing Model"""
+__docformat__ = "numpy"
+
+import logging
+from typing import Any, Tuple, Union
+
+import numpy as np
+import pandas as pd
+from darts import TimeSeries
+from darts.models import ExponentialSmoothing
+from darts.dataprocessing.transformers import MissingValuesFiller
+from darts.utils.utils import ModelMode, SeasonalityMode
+from darts.metrics import mape
+
+from openbb_terminal.decorators import log_start_end
+from openbb_terminal.rich_config import console
+
+
+TRENDS = ["N", "A", "M"]
+SEASONS = ["N", "A", "M"]
+PERIODS = [4, 5, 7]
+DAMPEN = ["T", "F"]
+
+logger = logging.getLogger(__name__)
+
+
+@log_start_end(log=logger)
+def get_expo_data(
+    data: Union[pd.Series, pd.DataFrame],
+    trend: str = "A",
+    seasonal: str = "A",
+    seasonal_periods: int = 7,
+    dampen: str = "F",
+    n_predict: int = 30,
+    start_window: float = 0.65,
+    forecast_horizon: int = 3,
+) -> Tuple[Any, Any, Any, Any, Any]:
+
+    """Performs Probabilistic Exponential Smoothing forecasting
+    This is a wrapper around statsmodels Holt-Winters' Exponential Smoothing;
+    we refer to this link for the original and more complete documentation of the parameters.
+
+    https://unit8co.github.io/darts/generated_api/darts.models.forecasting.exponential_smoothing.html?highlight=exponential
+
+    Parameters
+    ----------
+    data : Union[pd.Series, np.ndarray]
+        Input data.
+    trend: str
+        Trend component.  One of [N, A, M]
+        Defaults to ADDITIVE.
+    seasonal: str
+        Seasonal component.  One of [N, A, M]
+        Defaults to ADDITIVE.
+    seasonal_periods: int
+        Number of seasonal periods in a year (7 for daily data)
+        If not set, inferred from frequency of the series.
+    dampen: str
+        Dampen the function
+    n_predict: int
+        Number of days to forecast
+    start_window: float
+        Size of sliding window from start of timeseries and onwards
+    forecast_horizon: int
+        Number of days to forecast when backtesting and retraining historical
+
+    Returns
+    -------
+    List[float]
+        Adjusted Data series
+    List[float]
+        List of predicted values
+    Any
+        Fit Prob. Expo model object.
+    """
+
+    filler = MissingValuesFiller()
+    data["date"] = data.index  # add temp column since we need to use index col for date
+    ticker_series = TimeSeries.from_dataframe(
+        data,
+        time_col="date",
+        value_cols=["AdjClose"],
+        freq="B",
+        fill_missing_dates=True,
+    )
+
+    ticker_series = filler.transform(ticker_series)
+    ticker_series = ticker_series.astype(np.float32)
+    _, val = ticker_series.split_before(0.85)
+
+    if trend == "M":
+        trend = ModelMode.MULTIPLICATIVE
+    elif trend == "N":
+        trend = ModelMode.NONE
+    else:  # Default
+        trend = ModelMode.ADDITIVE
+
+    if seasonal == "M":
+        seasonal = SeasonalityMode.MULTIPLICATIVE
+    elif seasonal == "N":
+        seasonal = SeasonalityMode.NONE
+    else:  # Default
+        seasonal = SeasonalityMode.ADDITIVE
+
+    damped = True
+    if dampen == "F":
+        damped = False
+
+    # Model Init
+    model_es = ExponentialSmoothing(
+        trend=trend,
+        seasonal=seasonal,
+        seasonal_periods=int(seasonal_periods),
+        damped=damped,
+        random_state=42,
+    )
+
+    # Training model based on historical backtesting
+    historical_fcast_es = model_es.historical_forecasts(
+        ticker_series,
+        start=float(start_window),
+        forecast_horizon=int(forecast_horizon),
+        verbose=True,
+    )
+
+    # Show forecast over validation # and then +n_predict afterwards sampled 10 times per point
+    probabilistic_forecast = model_es.predict(int(n_predict), num_samples=500)
+    precision = mape(val, probabilistic_forecast)  # mape = mean average precision error
+    console.print(f"model {model_es} obtains MAPE: {precision:.2f}% \n")  # TODO
+
+    return (
+        ticker_series,
+        historical_fcast_es,
+        probabilistic_forecast,
+        precision,
+        model_es,
+    )
--- a/openbb_terminal/common/prediction_techniques/expo_view.py
+++ b/openbb_terminal/common/prediction_techniques/expo_view.py
@@ -0,0 +1,118 @@
+"""Probabilistic Exponential Smoothing View"""
+__docformat__ = "numpy"
+
+import logging
+import os
+from typing import Union
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+from openbb_terminal.config_terminal import theme
+from openbb_terminal.common.prediction_techniques import expo_model
+from openbb_terminal.config_plot import PLOT_DPI
+from openbb_terminal.decorators import log_start_end
+from openbb_terminal.helper_funcs import (
+    export_data,
+    plot_autoscale,
+)
+from openbb_terminal.rich_config import console
+from openbb_terminal.common.prediction_techniques.pred_helper import (
+    print_pretty_prediction,
+)
+
+logger = logging.getLogger(__name__)
+# pylint: disable=too-many-arguments
+
+
+@log_start_end(log=logger)
+def display_expo_forecast(
+    data: Union[pd.DataFrame, pd.Series],
+    ticker_name: str,
+    trend: str,
+    seasonal: str,
+    seasonal_periods: int,
+    dampen: str,
+    n_predict: int,
+    start_window: float,
+    forecast_horizon: int,
+    export: str = "",
+):
+    """Display Probabilistic Exponential Smoothing forecast
+
+    Parameters
+    ----------
+    data : Union[pd.Series, np.array]
+        Data to forecast
+    trend: str
+        Trend component.  One of [N, A, M]
+        Defaults to ADDITIVE.
+    seasonal: str
+        Seasonal component.  One of [N, A, M]
+        Defaults to ADDITIVE.
+    seasonal_periods: int
+        Number of seasonal periods in a year
+        If not set, inferred from frequency of the series.
+    dampen: str
+        Dampen the function
+    n_predict: int
+        Number of days to forecast
+    start_window: float
+        Size of sliding window from start of timeseries and onwards
+    forecast_horizon: int
+        Number of days to forecast when backtesting and retraining historical
+    export: str
+        Format to export data
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (2 axis is expected in the list), by default None
+    """
+    (
+        ticker_series,
+        historical_fcast_es,
+        predicted_values,
+        precision,
+        _,
+    ) = expo_model.get_expo_data(
+        data,
+        trend,
+        seasonal,
+        seasonal_periods,
+        dampen,
+        n_predict,
+        start_window,
+        forecast_horizon,
+    )
+
+    # Plotting with Matplotlib
+    external_axes = None
+    if not external_axes:
+        fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
+    else:
+        if len(external_axes) != 1:
+            logger.error("Expected list of one axis item.")
+            console.print("[red]Expected list of one axis item.\n[/red]")
+            return
+        ax = external_axes
+
+    # ax = fig.get_axes()[0] # fig gives list of axes (only one for this case)
+    ticker_series.plot(label="Actual AdjClose", figure=fig)
+    historical_fcast_es.plot(
+        label="Back-test 3-Days ahead forecast (Exp. Smoothing)", figure=fig
+    )
+    predicted_values.plot(
+        label="Probabilistic Forecast", low_quantile=0.1, high_quantile=0.9, figure=fig
+    )
+    ax.set_title(
+        f"PES for ${ticker_name} for next [{n_predict}] days (Model MAPE={round(precision,2)}%)"
+    )
+    ax.set_ylabel("Adj. Closing")
+    ax.set_xlabel("Date")
+    theme.style_primary_axis(ax)
+
+    if not external_axes:
+        theme.visualize_output()
+
+    numeric_forecast = predicted_values.quantile_df()["AdjClose_0.5"].tail(n_predict)
+    print_pretty_prediction(numeric_forecast, data["AdjClose"].iloc[-1])
+
+    export_data(export, os.path.dirname(os.path.abspath(__file__)), "expo")
--- a/openbb_terminal/common/prediction_techniques/pred_helper.py
+++ b/openbb_terminal/common/prediction_techniques/pred_helper.py
@@ -557,7 +557,6 @@ def lambda_price_prediction_color(val: float, last_val: float) -> str:

 def print_pretty_prediction(df_pred: pd.DataFrame, last_price: float):
    """Print predictions"""
-    console.print("")
    if rich_config.USE_COLOR:
        df_pred = pd.DataFrame(df_pred)
        df_pred.columns = ["pred"]
--- a/openbb_terminal/stocks/prediction_techniques/pred_api.py
+++ b/openbb_terminal/stocks/prediction_techniques/pred_api.py
@@ -35,5 +35,9 @@ from openbb_terminal.common.prediction_techniques.mc_view import (
    display_mc_forecast as mc,
 )

+from openbb_terminal.common.prediction_techniques.expo_view import (
+    display_expo_forecast as expo,
+)
+
 # Models
 models = _models(os.path.abspath(os.path.dirname(prediction_techniques.__file__)))
--- a/openbb_terminal/stocks/prediction_techniques/pred_controller.py
+++ b/openbb_terminal/stocks/prediction_techniques/pred_controller.py
@@ -22,6 +22,8 @@ from openbb_terminal.common.prediction_techniques import (
    neural_networks_view,
    pred_helper,
    regression_view,
+    expo_view,
+    expo_model,
 )
 from openbb_terminal.decorators import log_start_end
 from openbb_terminal.helper_funcs import (
@@ -54,6 +56,7 @@ class PredictionTechniquesController(BaseController):
        "lstm",
        "conv1d",
        "mc",
+        "expo",
    ]

    PATH = "/stocks/pred/"
@@ -90,6 +93,10 @@ class PredictionTechniquesController(BaseController):
            choices["ets"]["-s"] = {c: {} for c in ets_model.SEASONS}
            choices["arima"]["-i"] = {c: {} for c in arima_model.ICS}
            choices["mc"]["--dist"] = {c: {} for c in mc_model.DISTRIBUTIONS}
+            choices["expo"]["-t"] = {c: {} for c in expo_model.TRENDS}
+            choices["expo"]["-s"] = {c: {} for c in expo_model.SEASONS}
+            choices["expo"]["-p"] = {c: {} for c in expo_model.PERIODS}
+            choices["expo"]["-dp"] = {c: {} for c in expo_model.DAMPEN}
            self.completer = NestedCompleter.from_nested_dict(choices)

    def print_help(self):
@@ -119,6 +126,7 @@ class PredictionTechniquesController(BaseController):
        mt.add_cmd("lstm")
        mt.add_cmd("conv1d")
        mt.add_cmd("mc")
+        mt.add_cmd("expo")
        console.print(text=mt.menu_text, menu="Stocks - Prediction Techniques")

    def custom_reset(self):
@@ -713,3 +721,99 @@ class PredictionTechniquesController(BaseController):
                use_log=ns_parser.dist == "lognormal",
                export=ns_parser.export,
            )
+
+    @log_start_end(log=logger)
+    def call_expo(self, other_args: List[str]):
+        """Process expo command"""
+        parser = argparse.ArgumentParser(
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+            add_help=False,
+            prog="expo",
+            description="""
+                Perform Probabilistic Exponential Smoothing forecast
+                Trend: N: None, A: Additive, M: Multiplicative
+                Seasonality: N: None, A: Additive, M: Multiplicative
+                Dampen: T: True, F: False
+            """,
+        )
+        parser.add_argument(
+            "-n",
+            "--n_days",
+            action="store",
+            dest="n_days",
+            type=check_positive,
+            default=5,
+            help="prediction days.",
+        )
+        parser.add_argument(
+            "-t",
+            "--trend",
+            action="store",
+            dest="trend",
+            choices=expo_model.TRENDS,
+            default="A",
+            help="Trend: N: None, A: Additive, M: Multiplicative.",
+        )
+        parser.add_argument(
+            "-s",
+            "--seasonal",
+            action="store",
+            dest="seasonal",
+            choices=expo_model.SEASONS,
+            default="A",
+            help="Seasonality: N: None, A: Additive, M: Multiplicative.",
+        )
+        parser.add_argument(
+            "-p",
+            "--periods",
+            action="store",
+            dest="seasonal_periods",
+            type=check_positive,
+            default=7,
+            help="Seasonal periods: 4: Quarterly, 7: Daily",
+        )
+        parser.add_argument(
+            "-d",
+            "--dampen",
+            action="store",
+            dest="dampen",
+            default="F",
+            help="Dampening",
+        )
+        parser.add_argument(
+            "-w",
+            "--window",
+            action="store",
+            dest="start_window",
+            default=0.65,
+            help="Start point for rolling training and forecast window. 0.0-1.0",
+        )
+        parser.add_argument(
+            "-f",
+            "--forecasthorizon",
+            action="store",
+            dest="forecast_horizon",
+            default=3,
+            help="Days/Points to forecast when training and performing historical back-testing",
+        )
+
+        ns_parser = parse_known_args_and_warn(
+            parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED
+        )
+
+        if ns_parser:
+            if self.target != "AdjClose":
+                console.print("Expo Prediction designed for AdjClose prices\n")
+
+            expo_view.display_expo_forecast(
+                data=self.stock,
+                ticker_name=self.ticker,
+                n_predict=ns_parser.n_days,
+                trend=ns_parser.trend,
+                seasonal=ns_parser.seasonal,
+                seasonal_periods=ns_parser.seasonal_periods,
+                dampen=ns_parser.dampen,
+                start_window=ns_parser.start_window,
+                forecast_horizon=ns_parser.forecast_horizon,
+                export=ns_parser.export,
+            )
--- a/openbb_terminal/stocks/stocks_helper.py
+++ b/openbb_terminal/stocks/stocks_helper.py
@@ -5,6 +5,7 @@ import logging
 import os
 from datetime import datetime, timedelta, date
 from typing import List, Union, Optional, Iterable
+import warnings

 import financedatabase as fd
 import matplotlib.pyplot as plt
@@ -1064,6 +1065,7 @@ def additional_info_about_ticker(ticker: str) -> str:
            extra_info += "\n[param]Currency: [/param]USD"
            extra_info += "\n[param]Market:   [/param]"
            calendar = mcal.get_calendar("NYSE")
+            warnings.filterwarnings("ignore")
            sch = calendar.schedule(
                start_date=(datetime.now() - timedelta(days=3)).strftime("%Y-%m-%d"),
                end_date=(datetime.now() + timedelta(days=3)).strftime("%Y-%m-%d"),
--- a/poetry.lock
+++ b/poetry.lock
@@ -1768,6 +1768,22 @@ category = "main"
 optional = true
 python-versions = "*"

+[[package]]
+name = "lightgbm"
+version = "3.3.2"
+description = "LightGBM Python Package"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+numpy = "*"
+scikit-learn = "!=0.22.0"
+scipy = "*"
+
+[package.extras]
+dask = ["dask[array] (>=2.0.0)", "dask[dataframe] (>=2.0.0)", "dask[distributed] (>=2.0.0)", "pandas"]
+
 [[package]]
 name = "linearmodels"
 version = "4.26"
@@ -1789,6 +1805,14 @@ scipy = ">=1.2"
 setuptools-scm = ">=6.4.2,<7.0.0"
 statsmodels = ">=0.11"

+[[package]]
+name = "llvmlite"
+version = "0.38.1"
+description = "lightweight wrapper around basic LLVM functionality"
+category = "main"
+optional = false
+python-versions = ">=3.7,<3.11"
+
 [[package]]
 name = "lxml"
 version = "4.8.0"
@@ -2132,6 +2156,19 @@ doc = ["sphinx (>=4.5)", "pydata-sphinx-theme (>=0.8.1)", "sphinx-gallery (>=0.1
 extra = ["lxml (>=4.6)", "pygraphviz (>=1.9)", "pydot (>=1.4.2)", "sympy (>=1.10)"]
 test = ["pytest (>=7.1)", "pytest-cov (>=3.0)", "codecov (>=2.1)"]

+[[package]]
+name = "nfoursid"
+version = "1.0.0"
+description = "Implementation of N4SID, Kalman filtering and state-space models"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+matplotlib = ">=3.3"
+numpy = ">=1.19"
+pandas = ">=1.1"
+
 [[package]]
 name = "nodeenv"
 version = "1.6.0"
@@ -2184,6 +2221,18 @@ jupyter-server = ">=1.8,<2.0"
 [package.extras]
 test = ["pytest", "pytest-tornasync", "pytest-console-scripts"]

+[[package]]
+name = "numba"
+version = "0.55.1"
+description = "compiling Python code using LLVM"
+category = "main"
+optional = false
+python-versions = ">=3.7,<3.11"
+
+[package.dependencies]
+llvmlite = ">=0.38.0rc1,<0.39"
+numpy = ">=1.18,<1.22"
+
 [[package]]
 name = "numpy"
 version = "1.21.2"
@@ -3830,6 +3879,24 @@ anyio = ">=3.0.0,<4"
 [package.extras]
 full = ["itsdangerous", "jinja2", "python-multipart", "pyyaml", "requests"]

+[[package]]
+name = "statsforecast"
+version = "0.5.5"
+description = "Time series forecasting suite using statistical models"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+numba = "*"
+numpy = "*"
+pandas = "*"
+scipy = "*"
+statsmodels = "*"
+
+[package.extras]
+ray = ["ray"]
+
 [[package]]
 name = "statsmodels"
 version = "0.13.2"
@@ -4083,6 +4150,17 @@ category = "main"
 optional = false
 python-versions = ">=3.5"

+[[package]]
+name = "torch"
+version = "1.11.0"
+description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
+category = "main"
+optional = false
+python-versions = ">=3.7.0"
+
+[package.dependencies]
+typing-extensions = "*"
+
 [[package]]
 name = "tornado"
 version = "6.1"
@@ -4264,6 +4342,37 @@ tzdata = {version = "*", markers = "platform_system == \"Windows\""}
 devenv = ["black", "pyroma", "pytest-cov", "zest.releaser"]
 test = ["pytest-mock (>=3.3)", "pytest (>=4.3)"]

+[[package]]
+name = "u8darts"
+version = "0.19.0"
+description = "A python library for easy manipulation and forecasting of time series."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+holidays = ">=0.11.1"
+ipython = ">=7.0.0"
+joblib = ">=0.16.0"
+lightgbm = ">=2.2.3"
+matplotlib = ">=3.3.0"
+nfoursid = ">=1.0.0"
+numpy = ">=1.19.0"
+pandas = ">=1.0.5"
+requests = ">=2.22.0"
+scikit-learn = ">=1.0.1"
+scipy = ">=1.3.2"
+statsforecast = ">=0.5.2"
+statsmodels = ">=0.13.0"
+tqdm = ">=4.60.0"
+xarray = ">=0.17.0"
+
+[package.extras]
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 [[package]]
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    {file = "statsmodels-0.13.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:066a75d5585378b2df972f81a90b9a3da5e567b7d4833300c1597438c1a35e29"},
@@ -7338,6 +7540,27 @@ toolz = [
    {file = "toolz-0.11.2-py3-none-any.whl", hash = "sha256:a5700ce83414c64514d82d60bcda8aabfde092d1c1a8663f9200c07fdcc6da8f"},
    {file = "toolz-0.11.2.tar.gz", hash = "sha256:6b312d5e15138552f1bda8a4e66c30e236c831b612b2bf0005f8a1df10a4bc33"},
 ]
+torch = [
+    {file = "torch-1.11.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:62052b50fffc29ca7afc0c04ef8206b6f1ca9d10629cb543077e12967e8d0398"},
+    {file = "torch-1.11.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:866bfba29ac98dec35d893d8e17eaec149d0ac7a53be7baae5c98069897db667"},
+    {file = "torch-1.11.0-cp310-cp310-win_amd64.whl", hash = "sha256:951640fb8db308a59d9b510e7d1ad910aff92913323bbe4bc75435347ddd346d"},
+    {file = "torch-1.11.0-cp310-none-macosx_10_9_x86_64.whl", hash = "sha256:5d77b5ece78fdafa5c7f42995ff9474399d22571cd6b2de21a5d666306a2ff8c"},
+    {file = "torch-1.11.0-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:b5a38682769b544c875ecc34bcb81fbad5c922139b61319aacffcfd8a32f528c"},
+    {file = "torch-1.11.0-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:f82d77695a60626f2b7382d85bc566de8a6b3e50d32080755abc040db802e419"},
+    {file = "torch-1.11.0-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:b96654d42566080a134e784705f33f8536b3b95b5dcde357ed7879b1692a5f78"},
+    {file = "torch-1.11.0-cp37-cp37m-win_amd64.whl", hash = "sha256:8ee7c2e8d7f7020d5bfbc1bb91b9591044c26bbd0cee5e4f694cfd7ed8649260"},
+    {file = "torch-1.11.0-cp37-none-macosx_10_9_x86_64.whl", hash = "sha256:6860b1d1bf0bb0b67a6bd47f85a0e4c825b518eea13b5d6101999dbbcbd5bc0c"},
+    {file = "torch-1.11.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:4322aa29f50da7f404db06cdf30896ea67b09f673af4a985afc7162bc897864d"},
+    {file = "torch-1.11.0-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:e4d2e0ddd652f30e94cff750220324ec45705d4ecc69658f773b3cb1c7a28dd0"},
+    {file = "torch-1.11.0-cp38-cp38-win_amd64.whl", hash = "sha256:34ce5ea4d8d85da32cdbadb50d4585106901e9f8a3527991daa70c13a09de1f7"},
+    {file = "torch-1.11.0-cp38-none-macosx_10_9_x86_64.whl", hash = "sha256:0ccc85cd06227a3edf809e2c795fd5762c3d4e8a38b5c9f744c6e7cf841361bb"},
+    {file = "torch-1.11.0-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:c1554e49d74f1b2c3e7202d77056ba2dd7465437585bac64062b580f714a44e9"},
+    {file = "torch-1.11.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:58c7814502b1c129a650d7092033bbb0bbd64faf1a7941631aaa1aeaddc37570"},
+    {file = "torch-1.11.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:831cf588f01dda9409e75576741d2823453990dee2983d670f2584b37a01adf7"},
+    {file = "torch-1.11.0-cp39-cp39-win_amd64.whl", hash = "sha256:44a1d02fd20f827f0f36dc26fdcfc45e793806a6ad52769a22260655a77a4369"},
+    {file = "torch-1.11.0-cp39-none-macosx_10_9_x86_64.whl", hash = "sha256:50fd9bf85c578c871c28f1cb0ace9dfc6024401c7f399b174fb0f370899f4454"},
+    {file = "torch-1.11.0-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:0e48af66ad755f0f9c5f2664028a414f57c49d6adc37e77e06fe0004da4edb61"},
+]
 tornado = [
    {file = "tornado-6.1-cp35-cp35m-macosx_10_9_x86_64.whl", hash = "sha256:d371e811d6b156d82aa5f9a4e08b58debf97c302a35714f6f45e35139c332e32"},
    {file = "tornado-6.1-cp35-cp35m-manylinux1_i686.whl", hash = "sha256:0d321a39c36e5f2c4ff12b4ed58d41390460f798422c4504e09eb5678e09998c"},
@@ -7445,6 +7668,10 @@ tzlocal = [
    {file = "tzlocal-4.2-py3-none-any.whl", hash = "sha256:89885494684c929d9191c57aa27502afc87a579be5cdd3225c77c463ea043745"},
    {file = "tzlocal-4.2.tar.gz", hash = "sha256:ee5842fa3a795f023514ac2d801c4a81d1743bbe642e3940143326b3a00addd7"},
 ]
+u8darts = [
+    {file = "u8darts-0.19.0-py3-none-any.whl", hash = "sha256:8b9073afa0bf36ec4036ca176fbe5426772475455e6cba46be1f63cb2197f953"},
+    {file = "u8darts-0.19.0.tar.gz", hash = "sha256:9cb55ab1ed8d634d93348ae046beadea4bdd12f2b2e49f1d1829841994d200ca"},
+]
 ujson = [
    {file = "ujson-5.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:754e9da96a24535ae5ab2a52e1d1dfc65a6a717c14063855b83f327fdf2173ea"},
    {file = "ujson-5.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6227597d0201ceadc902d1a8edaffaeb244050b197368ed25e6f6be0df170a6f"},
@@ -7680,6 +7907,10 @@ wsproto = [
    {file = "wsproto-1.1.0-py3-none-any.whl", hash = "sha256:2218cb57952d90b9fca325c0dcfb08c3bda93e8fd8070b0a17f048e2e47a521b"},
    {file = "wsproto-1.1.0.tar.gz", hash = "sha256:a2e56bfd5c7cd83c1369d83b5feccd6d37798b74872866e62616e0ecf111bda8"},
 ]
+xarray = [
+    {file = "xarray-2022.3.0-py3-none-any.whl", hash = "sha256:560f36eaabe7a989d5583d37ec753dd737357aa6a6453e55c80bb4f92291a69e"},
+    {file = "xarray-2022.3.0.tar.gz", hash = "sha256:398344bf7d170477aaceff70210e11ebd69af6b156fe13978054d25c48729440"},
+]
 xlsxwriter = [
    {file = "XlsxWriter-3.0.3-py3-none-any.whl", hash = "sha256:df0aefe5137478d206847eccf9f114715e42aaea077e6a48d0e8a2152e983010"},
    {file = "XlsxWriter-3.0.3.tar.gz", hash = "sha256:e89f4a1d2fa2c9ea15cde77de95cd3fd8b0345d0efb3964623f395c8c4988b7f"},
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,6 +14,7 @@ openbb = 'terminal:terminal'
 [tool.poetry.dependencies]
 python = "^3.8,<3.10"
 iso8601 = "^0.1.14"
+FundamentalAnalysis = "^0.2.6"
 requests = "^2.25.1"
 alpha-vantage = "^2.3.1"
 finviz = "^1.3.4"
@@ -102,7 +103,10 @@ Jinja2 = "3.0.3"  # Remove pinned version when Voila supports 3.1
 click = "8.0.1"  # Remove pinned version when Black supports 8.1
 kaleido = {version = "<=0.2.1", optional = true, extras = ["bots"]}
 Riskfolio-Lib = "^3.1.1"
-fundamentalanalysis = "^0.2.14"
+llvmlite = {version = "^0.38.1", extras = ["prediction"]}
+torch = {version = "^1.11.0", extras = ["prediction"]}
+u8darts = {extras = ["prediction"], version = "^0.19.0"}
+fundamentalanalysis = {version = "^0.2.14", extras = ["prediction"]}


 [tool.poetry.dev-dependencies]
--- a/requirements-full.txt
+++ b/requirements-full.txt
@@ -36,7 +36,7 @@ cfgv==3.3.1; python_full_version >= "3.6.1" and python_version >= "3.7"
 charset-normalizer==2.0.12; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "4.0" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6")
 click==8.0.1; python_version >= "3.6"
 codespell==2.1.0; python_version >= "3.5"
-colorama==0.4.4; python_full_version >= "3.6.2" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and platform_system == "Windows" and python_version >= "3.8" and (python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.5.0") and sys_platform == "win32" and (python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" or platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "3.6" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows" and python_version >= "3.6") and (python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0")
+colorama==0.4.4; python_full_version >= "3.6.2" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and platform_system == "Windows" and python_version >= "3.8" and (python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.5.0") and sys_platform == "win32" and (python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" or platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.5.0") and (python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0")
 commonmark==0.9.1; python_full_version >= "3.6.2" and python_full_version < "4.0.0"
 convertdate==2.4.0; python_version >= "3.7" and python_version < "4"
 coverage==6.3.2; python_version >= "3.7"
@@ -88,7 +88,7 @@ google-pasta==0.2.0
 grpcio==1.45.0; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.6"
 h11==0.13.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0"
 h5py==3.6.0; python_version >= "3.7"
-hijri-converter==2.2.3; python_version >= "3.6"
+hijri-converter==2.2.3; python_version >= "3.7"
 holidays==0.11.3.1; python_version >= "3.6"
 identify==2.4.12; python_version >= "3.7"
 idna==3.3; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "4" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6")
@@ -125,7 +125,9 @@ kiwisolver==1.4.2; python_version >= "3.7"
 korean-lunar-calendar==0.2.1; python_version >= "3.7" and python_full_version >= "3.7.0"
 lazy-object-proxy==1.7.1; python_version >= "3.6" and python_full_version >= "3.6.2"
 libclang==11.1.0
+lightgbm==3.3.2; python_version >= "3.7"
 linearmodels==4.26; python_version >= "3.8"
+llvmlite==0.38.1; python_version >= "3.7" and python_version < "3.11"
 lxml==4.8.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
 markdown-it-py==1.1.0; python_version >= "3.6" and python_version < "4.0"
 markdown==3.3.6; python_version >= "3.6"
@@ -149,9 +151,11 @@ nbconvert==6.5.0; python_version >= "3.7"
 nbformat==5.3.0; python_full_version >= "3.7.0" and python_version >= "3.7"
 nest-asyncio==1.5.5; python_full_version >= "3.7.0" and python_version >= "3.7"
 networkx==2.8; python_version >= "3.8"
+nfoursid==1.0.0; python_version >= "3.7"
 nodeenv==1.6.0; python_version >= "3.7"
 notebook-shim==0.1.0; python_version >= "3.7"
 notebook==6.4.11; python_version >= "3.7"
+numba==0.55.1; python_version >= "3.7" and python_version < "3.11"
 numpy==1.21.2; python_version >= "3.7" and python_version < "3.11"
 oandapyv20==0.6.3
 oauthlib==3.2.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
@@ -270,6 +274,7 @@ sphinxcontrib-serializinghtml==1.1.5; python_version >= "3.6"
 squarify==0.4.3
 sseclient==0.0.27
 stack-data==0.2.0; python_full_version >= "3.6.2" and python_version >= "3.8"
+statsforecast==0.5.5; python_version >= "3.7"
 statsmodels==0.13.2; python_version >= "3.7"
 stevedore==3.5.0; python_version >= "3.7"
 tabulate==0.8.9; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0"
@@ -291,8 +296,9 @@ tokenize-rt==4.2.1; python_full_version >= "3.6.2" and python_version >= "3.7"
 toml==0.10.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7"
 tomli==2.0.1; python_version >= "3.8" and python_full_version >= "3.6.2" and python_version < "3.11"
 toolz==0.11.2; python_version >= "3.7" and python_full_version >= "3.7.0"
+torch==1.11.0; python_full_version >= "3.7.0"
 tornado==6.1; python_full_version >= "3.7.0" and python_version >= "3.7"
-tqdm==4.64.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
+tqdm==4.64.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
 tradingview-ta==3.2.10; python_version >= "3.6"
 traitlets==5.1.1; python_full_version >= "3.7.0" and python_version >= "3.8"
 trio-websocket==0.9.2; python_version >= "3.7" and python_version < "4.0"
@@ -304,9 +310,10 @@ types-requests==2.27.20
 types-setuptools==57.4.14
 types-six==1.16.15
 types-urllib3==1.26.13
-typing-extensions==4.2.0; python_version < "3.10" and python_full_version >= "3.6.2" and python_version >= "3.7"
+typing-extensions==4.2.0; python_version < "3.10" and python_full_version >= "3.7.0" and python_version >= "3.7"
 tzdata==2022.1; platform_system == "Windows" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6")
 tzlocal==4.2; python_version >= "3.6"
+u8darts==0.19.0; python_version >= "3.7"
 ujson==5.2.0; python_version >= "3.7"
 unidecode==1.3.4; python_version >= "3.7"
 update-checker==0.18.0; python_version >= "3.6" and python_version < "4.0"
@@ -325,6 +332,7 @@ werkzeug==2.1.1; python_version >= "3.7"
 widgetsnbextension==3.6.0
 wrapt==1.14.0; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and python_version >= "3.8"
 wsproto==1.1.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0"
+xarray==2022.3.0; python_version >= "3.8"
 xlsxwriter==3.0.3; python_version >= "3.7"
 yarl==1.7.2; python_version >= "3.6"
 yfinance==0.1.70
--- a/requirements.txt
+++ b/requirements.txt
@@ -27,7 +27,7 @@ certifi==2021.10.8; python_full_version >= "3.7.1" and python_version < "4" and
 cffi==1.15.0; python_version >= "3.7"
 charset-normalizer==2.0.12; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "4.0" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7")
 click==8.0.1; python_version >= "3.6"
-colorama==0.4.4; python_full_version >= "3.6.2" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and platform_system == "Windows" and python_version >= "3.8" and (python_version >= "3.6" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows" and python_version >= "3.6") and sys_platform == "win32"
+colorama==0.4.4; python_full_version >= "3.6.2" and python_full_version < "4.0.0" and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.5") and platform_system == "Windows" and python_version >= "3.8" and (python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" or python_full_version >= "3.5.0" and platform_system == "Windows" and python_version >= "3.7") and sys_platform == "win32"
 commonmark==0.9.1; python_full_version >= "3.6.2" and python_full_version < "4.0.0"
 convertdate==2.4.0; python_version >= "3.7" and python_version < "4"
 cryptography==36.0.2; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "3.7"
@@ -67,7 +67,7 @@ gitdb==4.0.9; python_version >= "3.7"
 gitpython==3.1.27; python_version >= "3.7"
 grpcio==1.45.0; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.6"
 h11==0.13.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0"
-hijri-converter==2.2.3; python_version >= "3.6"
+hijri-converter==2.2.3; python_version >= "3.7"
 holidays==0.11.3.1; python_version >= "3.6"
 idna==3.3; python_full_version >= "3.7.1" and python_version >= "3.8" and python_version < "4" and python_full_version < "4.0.0" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7")
 importlib-metadata==4.11.3; python_version < "3.10" and python_version >= "3.7"
@@ -96,7 +96,9 @@ jupyterlab-widgets==1.1.0; python_version >= "3.6"
 jupyterlab==3.3.4; python_version >= "3.7"
 kiwisolver==1.4.2; python_version >= "3.7"
 korean-lunar-calendar==0.2.1; python_version >= "3.7" and python_full_version >= "3.7.0"
+lightgbm==3.3.2; python_version >= "3.7"
 linearmodels==4.26; python_version >= "3.8"
+llvmlite==0.38.1; python_version >= "3.7" and python_version < "3.11"
 lxml==4.8.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7"
 markupsafe==2.1.1; python_version >= "3.7"
 matplotlib-inline==0.1.3; python_full_version >= "3.6.2" and python_version >= "3.8"
@@ -114,8 +116,10 @@ nbconvert==6.5.0; python_version >= "3.7"
 nbformat==5.3.0; python_full_version >= "3.7.0" and python_version >= "3.7"
 nest-asyncio==1.5.5; python_full_version >= "3.7.0" and python_version >= "3.7"
 networkx==2.8; python_version >= "3.8"
+nfoursid==1.0.0; python_version >= "3.7"
 notebook-shim==0.1.0; python_version >= "3.7"
 notebook==6.4.11; python_version >= "3.7"
+numba==0.55.1; python_version >= "3.7" and python_version < "3.11"
 numpy==1.21.2; python_version >= "3.7" and python_version < "3.11"
 oandapyv20==0.6.3
 oauthlib==3.2.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
@@ -211,6 +215,7 @@ soupsieve==2.3.2.post1; python_version >= "3.6" and python_full_version >= "3.6.
 squarify==0.4.3
 sseclient==0.0.27
 stack-data==0.2.0; python_full_version >= "3.6.2" and python_version >= "3.8"
+statsforecast==0.5.5; python_version >= "3.7"
 statsmodels==0.13.2; python_version >= "3.7"
 tabulate==0.8.9; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0"
 temporal-cache==0.1.4
@@ -222,15 +227,17 @@ threadpoolctl==3.1.0; python_version >= "3.7" and python_full_version < "3.0.0"
 tinycss2==1.1.1; python_version >= "3.7"
 tomli==2.0.1; python_version >= "3.8" and python_full_version >= "3.6.2"
 toolz==0.11.2; python_version >= "3.7" and python_full_version >= "3.7.0"
+torch==1.11.0; python_full_version >= "3.7.0"
 tornado==6.1; python_full_version >= "3.7.0" and python_version >= "3.7"
-tqdm==4.64.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
+tqdm==4.64.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
 tradingview-ta==3.2.10; python_version >= "3.6"
 traitlets==5.1.1; python_full_version >= "3.7.0" and python_version >= "3.8"
 trio-websocket==0.9.2; python_version >= "3.7" and python_version < "4.0"
 trio==0.20.0; python_version >= "3.7" and python_version < "4.0"
-typing-extensions==4.2.0; python_version < "3.10" and python_full_version >= "3.6.2" and python_version >= "3.7"
+typing-extensions==4.2.0; python_version < "3.10" and python_full_version >= "3.7.0" and python_version >= "3.7"
 tzdata==2022.1; platform_system == "Windows" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6")
 tzlocal==4.2; python_version >= "3.6"
+u8darts==0.19.0; python_version >= "3.7"
 ujson==5.2.0; python_version >= "3.7"
 unidecode==1.3.4; python_version >= "3.7"
 update-checker==0.18.0; python_version >= "3.6" and python_version < "4.0"
@@ -246,6 +253,7 @@ websockets==10.3; python_version >= "3.7"
 widgetsnbextension==3.6.0
 wrapt==1.14.0; python_full_version >= "3.7.1" and python_full_version < "4.0.0" and python_version >= "3.8"
 wsproto==1.1.0; python_version >= "3.7" and python_version < "4.0" and python_full_version >= "3.7.0"
+xarray==2022.3.0; python_version >= "3.8"
 xlsxwriter==3.0.3; python_version >= "3.7"
 yarl==1.7.2; python_version >= "3.6"
 yfinance==0.1.70
--- a/scripts/test_stocks_pred.openbb
+++ b/scripts/test_stocks_pred.openbb
@@ -11,4 +11,5 @@ rnn
 lstm
 conv1d
 mc
+expo
 exit
--- a/website/content/terminal/common/prediction_techniques/expo/_index.md
+++ b/website/content/terminal/common/prediction_techniques/expo/_index.md
@@ -0,0 +1,30 @@
+```
+usage: ets [-n N_DAYS] [-t TREND] [-s SEASONAL] [-p SEASONAL_PERIODS] [-e S_END_DATE] [-d DAMPED][-w START_WINDOW][-f FORECAST_HORIZON][-h]
+```
+
+Performs Probabalistic Exponential Smoothing forecasting. This is a wrapper around statsmodels Holt-Winters' Exponential Smoothing;
+https://unit8co.github.io/darts/generated_api/darts.models.forecasting.exponential_smoothing.html
+
+
+```
+optional arguments:
+  -n N_DAYS, --n_days N_DAYS
+                        prediction days. (default: 5)
+  -t {N,A,M}, --trend {N,A,M}
+                        Trend: N: None, A: Additive, M: Multiplicative. (default: A)
+  -s {N,A,M}, --seasonal {N,A,M}
+                        Seasonality: N: None, A: Additive, M: Multiplicative. (default: A)
+  -p SEASONAL_PERIODS, --periods SEASONAL_PERIODS
+                        Seasonal periods: 4: Quarters, 5: Business Days, 7: Weekly (default: 5)
+  -d DAMPED, --damped DAMPED
+                        Dampening (default: F)
+  -w START_WINDOW, --window START_WINDOW
+                        Start point for rolling training and forecast window. 0.0-1.0 (default: 0.65)
+  -f FORECAST_HORIZON, --forecasthorizon FORECAST_HORIZON
+                        Days/Points to forecast when training and performing historical back-testing
+                        (default: 3)
+  -h, --help            show this help message (default: False)
+  --export EXPORT       Export figure into png, jpg, pdf, svg (default: )
+```
+
+![EXPO](https://user-images.githubusercontent.com/105685594/169634909-30864d44-e607-4e6f-8d59-ac49dafa2e2c.png)
--- a/website/data/menu/main.yml
+++ b/website/data/menu/main.yml
@@ -660,6 +660,8 @@ main:
                  ref: "/terminal/common/prediction_techniques/conv1d"
                - name: mc
                  ref: "/terminal/common/prediction_techniques/mc"
+                - name: expo
+                  ref: "/terminal/common/prediction_techniques/expo"
        - name: Cryptocurrency
          ref: "/terminal/cryptocurrency"
          sub:
@@ -1654,4 +1656,4 @@ main:
      - name: "Slack"
        ref: "/bots/slack"
      - name: "GroupMe"
-        ref: "/bots/groupme"
+        ref: "/bots/groupme"