Cyclical features in time series forecasting¶

Cyclical features play an important role in time series prediction because they capture recurring patterns or oscillations within a data set. These patterns repeat at fixed intervals, and the effective incorporation of cyclical features into a machine learning model requires careful preprocessing and feature engineering.

Due to the circular nature of cyclical features, it is not recommended to use them directly as numerical inputs in a machine learning model. Instead, they should be encoded in a format that captures their cyclical behavior. There are several common encoding techniques:

• One-hot encoding: If the cyclical feature consists of distinct categories, such as seasons or months, one-hot encoding can be used. This approach creates binary variables for each category, allowing the model to understand the presence or absence of specific categories.

• Trigonometric coding: For periodic features such as time of day or day of the week, trigonometric functions such as sine and cosine can be used for coding. By mapping the cyclic feature onto a unit circle, these functions preserve the cyclic relationships. In addition, this method introduces only two additional features, making it an efficient coding technique.

• Basis functions: Basis functions are mathematical functions that span a vector space and can be used to represent other functions within that space. When using basis functions, the cyclic feature is transformed into a new set of features based on the selected basis functions. Some commonly used basis functions for encoding cyclic features include Fourier basis functions, B-spline basis functions, and Gaussian basis functions. B-splines are a way to approximate nonlinear functions using a piecewise combination of polynomials.

By applying these encoding techniques, cyclic features can be effectively incorporated into a machine learning model, allowing it to capture and exploit the valuable recurring patterns present in time series data.

✎ Note

The following examples are inspired by Time-related feature engineering, scikit-lego’s documentation and Three Approaches to Encoding Time Information as Features for ML Models By Eryk Lewinson.

Libraries¶

In [6]:

# Data manipulation
# ==============================================================================
import numpy as np
import pandas as pd

# Plots
# ==============================================================================
import matplotlib.pyplot as plt
plt.style.use('seaborn-v0_8-darkgrid')

# Modelling and Forecasting
# ==============================================================================
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import SplineTransformer
from sklearn.compose import make_column_transformer
from sklego.preprocessing import RepeatingBasisFunction
from skforecast.ForecasterAutoreg import ForecasterAutoreg
from skforecast.model_selection import backtesting_forecaster

# Warnings configuration
# ==============================================================================
import warnings

# Data manipulation # ============================================================================== import numpy as np import pandas as pd # Plots # ============================================================================== import matplotlib.pyplot as plt plt.style.use('seaborn-v0_8-darkgrid') # Modelling and Forecasting # ============================================================================== from sklearn.ensemble import HistGradientBoostingRegressor from sklearn.preprocessing import FunctionTransformer from sklearn.preprocessing import OneHotEncoder from sklearn.preprocessing import SplineTransformer from sklearn.compose import make_column_transformer from sklego.preprocessing import RepeatingBasisFunction from skforecast.ForecasterAutoreg import ForecasterAutoreg from skforecast.model_selection import backtesting_forecaster # Warnings configuration # ============================================================================== import warnings

Data¶

In [7]:

# Data simulation
# ==============================================================================
np.random.seed(123)
dates = pd.date_range(start="2020-01-01", end="2023-12-31")
data = pd.DataFrame(index=dates)
data.index.name = "date"
data["day_idx"] = range(len(data))
data['month'] = data.index.month

# Create the components that will be combined to get the target series
signal_1 = 3 + 4 * np.sin(data["day_idx"] / 365 * 2 * np.pi)
signal_2 = 3 * np.sin(data["day_idx"] / 365 * 4 * np.pi + 365/2)
noise = np.random.normal(0, 0.85, len(data))
y = signal_1 + signal_2 + noise

data["y"] = y
data = data[["y", "month"]]
data.head(3)

# Data simulation # ============================================================================== np.random.seed(123) dates = pd.date_range(start="2020-01-01", end="2023-12-31") data = pd.DataFrame(index=dates) data.index.name = "date" data["day_idx"] = range(len(data)) data['month'] = data.index.month # Create the components that will be combined to get the target series signal_1 = 3 + 4 * np.sin(data["day_idx"] / 365 * 2 * np.pi) signal_2 = 3 * np.sin(data["day_idx"] / 365 * 4 * np.pi + 365/2) noise = np.random.normal(0, 0.85, len(data)) y = signal_1 + signal_2 + noise data["y"] = y data = data[["y", "month"]] data.head(3)

Out[7]:

	y	month
date
2020-01-01	2.928244	1
2020-01-02	4.866145	1
2020-01-03	4.425159	1

In [8]:

# Split train-test
# ==============================================================================
end_train = '2022-06-30 23:59:00'
data_train = data.loc[: end_train, :]
data_test  = data.loc[end_train:, :]

print(f"Dates train : {data_train.index.min()} --- {data_train.index.max()}  (n={len(data_train)})")
print(f"Dates test  : {data_test.index.min()} --- {data_test.index.max()}  (n={len(data_test)})")

# Split train-test # ============================================================================== end_train = '2022-06-30 23:59:00' data_train = data.loc[: end_train, :] data_test = data.loc[end_train:, :] print(f"Dates train : {data_train.index.min()} --- {data_train.index.max()} (n={len(data_train)})") print(f"Dates test : {data_test.index.min()} --- {data_test.index.max()} (n={len(data_test)})")

Dates train : 2020-01-01 00:00:00 --- 2022-06-30 00:00:00  (n=912)
Dates test  : 2022-07-01 00:00:00 --- 2023-12-31 00:00:00  (n=549)

In [9]:

# Plot time series
# ==============================================================================
fig, ax=plt.subplots(figsize=(9, 3))
data_train['y'].plot(title="Time series", label="train", ax=ax)
data_test['y'].plot(title="Time series", label="test", ax=ax)
ax.legend();

# Plot time series # ============================================================================== fig, ax=plt.subplots(figsize=(9, 3)) data_train['y'].plot(title="Time series", label="train", ax=ax) data_test['y'].plot(title="Time series", label="test", ax=ax) ax.legend();

One hot encoding¶

In [10]:

# One hot encoding of week_day and hour_day
# ==============================================================================
one_hot_encoder = make_column_transformer(
                      (
                          OneHotEncoder(sparse_output=False, drop='if_binary'),
                          ['month'],
                      ),
                      remainder="passthrough",
                      verbose_feature_names_out=False,
                  ).set_output(transform="pandas")

data_encoded_oh = one_hot_encoder.fit_transform(data)
data_encoded_oh.head(3)

# One hot encoding of week_day and hour_day # ============================================================================== one_hot_encoder = make_column_transformer( ( OneHotEncoder(sparse_output=False, drop='if_binary'), ['month'], ), remainder="passthrough", verbose_feature_names_out=False, ).set_output(transform="pandas") data_encoded_oh = one_hot_encoder.fit_transform(data) data_encoded_oh.head(3)

Out[10]:

	month_1	month_2	month_3	month_4	month_5	month_6	month_7	month_8	month_9	month_10	month_11	month_12	y
date
2020-01-01	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.928244
2020-01-02	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.866145
2020-01-03	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.425159

Sine-Cosine encoding¶

In [11]:

# Cyclical encoding with sine/cosine transformation
# ==============================================================================
def sin_transformer(period):
	"""
	Returns a transformer that applies sine transformation to a variable using
	the specified period.
	"""
	return FunctionTransformer(lambda x: np.sin(x / period * 2 * np.pi))

def cos_transformer(period):
	"""
	Returns a transformer that applies cosine transformation to a variable using
	the specified period.
	"""
	return FunctionTransformer(lambda x: np.cos(x / period * 2 * np.pi))

data_encoded_sin_cos = data.copy()
data_encoded_sin_cos["month_sin"] = sin_transformer(12).fit_transform(data_encoded_sin_cos['month'])
data_encoded_sin_cos["month_cos"] = cos_transformer(12).fit_transform(data_encoded_sin_cos['month'])
data_encoded_sin_cos.head()

# Cyclical encoding with sine/cosine transformation # ============================================================================== def sin_transformer(period): """ Returns a transformer that applies sine transformation to a variable using the specified period. """ return FunctionTransformer(lambda x: np.sin(x / period * 2 * np.pi)) def cos_transformer(period): """ Returns a transformer that applies cosine transformation to a variable using the specified period. """ return FunctionTransformer(lambda x: np.cos(x / period * 2 * np.pi)) data_encoded_sin_cos = data.copy() data_encoded_sin_cos["month_sin"] = sin_transformer(12).fit_transform(data_encoded_sin_cos['month']) data_encoded_sin_cos["month_cos"] = cos_transformer(12).fit_transform(data_encoded_sin_cos['month']) data_encoded_sin_cos.head()

Out[11]:

	y	month	month_sin	month_cos
date
2020-01-01	2.928244	1	0.5	0.866025
2020-01-02	4.866145	1	0.5	0.866025
2020-01-03	4.425159	1	0.5	0.866025
2020-01-04	3.069222	1	0.5	0.866025
2020-01-05	4.021290	1	0.5	0.866025

In [12]:

# Plot of the transformation
# ==============================================================================
fig, ax = plt.subplots(figsize=(4., 3.5))
sp = ax.scatter(
        data_encoded_sin_cos["month_sin"],
        data_encoded_sin_cos["month_cos"],
        c=data_encoded_sin_cos["month"],
        cmap='viridis'
     )
ax.set(
    xlabel="sin(month)",
    ylabel="cos(month)",
)
_ = fig.colorbar(sp)
data_encoded_sin_cos = data_encoded_sin_cos.drop(columns='month')

# Plot of the transformation # ============================================================================== fig, ax = plt.subplots(figsize=(4., 3.5)) sp = ax.scatter( data_encoded_sin_cos["month_sin"], data_encoded_sin_cos["month_cos"], c=data_encoded_sin_cos["month"], cmap='viridis' ) ax.set( xlabel="sin(month)", ylabel="cos(month)", ) _ = fig.colorbar(sp) data_encoded_sin_cos = data_encoded_sin_cos.drop(columns='month')

B-splines functions¶

In [13]:

# Create feature day of year
# ==============================================================================
data['day_of_year'] = data.index.day_of_year
data.head(3)

# Create feature day of year # ============================================================================== data['day_of_year'] = data.index.day_of_year data.head(3)

Out[13]:

	y	month	day_of_year
date
2020-01-01	2.928244	1	1
2020-01-02	4.866145	1	2
2020-01-03	4.425159	1	3

In [14]:

# B-spline functions
# ==============================================================================
def spline_transformer(period, degree=3, extrapolation="periodic"):
    """
    Returns a transformer that applies B-spline transformation.
    """
    return SplineTransformer(
               degree        = degree,
               n_knots       = period+1,
               knots         = 'uniform',
               extrapolation = extrapolation,
               include_bias  = True
           ).set_output(transform="pandas")

splines_month = spline_transformer(period=12).fit_transform(data[['day_of_year']])
splines_month.columns = [f"spline{i}" for i in range(len(splines_month.columns))]

# B-spline functions # ============================================================================== def spline_transformer(period, degree=3, extrapolation="periodic"): """ Returns a transformer that applies B-spline transformation. """ return SplineTransformer( degree = degree, n_knots = period+1, knots = 'uniform', extrapolation = extrapolation, include_bias = True ).set_output(transform="pandas") splines_month = spline_transformer(period=12).fit_transform(data[['day_of_year']]) splines_month.columns = [f"spline{i}" for i in range(len(splines_month.columns))]

The graph below shows the 12 spline functions generated using the day of the year as input. Since 12 splines are created with knots evenly distributed along the range 1 to 365 (day_of_year), each curve represents the proximity to the beginning of a particular month.

In [15]:

# Location of the maximum value of each spline
# ==============================================================================
splines_month.idxmax()

# Location of the maximum value of each spline # ============================================================================== splines_month.idxmax()

Out[15]:

spline0    2020-12-01
spline1    2020-01-01
spline2    2020-01-31
spline3    2020-03-02
spline4    2020-04-01
spline5    2020-05-02
spline6    2020-06-01
spline7    2020-07-02
spline8    2020-08-01
spline9    2020-08-31
spline10   2020-10-01
spline11   2020-10-31
dtype: datetime64[ns]

In [16]:

# Plot of the B-splines functions for the first 365 days
# ==============================================================================
fig, ax = plt.subplots(figsize=(8, 5))
splines_month.head(365).plot(
    ax       = ax,
    subplots = True,
    sharex   = True,
    legend   = False,
    yticks   = [],
    title    = 'Splines functions for the first 365 days'
);

# Plot of the B-splines functions for the first 365 days # ============================================================================== fig, ax = plt.subplots(figsize=(8, 5)) splines_month.head(365).plot( ax = ax, subplots = True, sharex = True, legend = False, yticks = [], title = 'Splines functions for the first 365 days' );

/tmp/ipykernel_5424/2664573984.py:4: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared.
  splines_month.head(365).plot(

In [17]:

# Encoded data
# ==============================================================================
data_encoded_splines = pd.concat([data, splines_month], axis=1)
data_encoded_splines = data_encoded_splines.drop(columns=['day_of_year', 'month'])
data_encoded_splines.head(3)

# Encoded data # ============================================================================== data_encoded_splines = pd.concat([data, splines_month], axis=1) data_encoded_splines = data_encoded_splines.drop(columns=['day_of_year', 'month']) data_encoded_splines.head(3)

Out[17]:

	y	spline0	spline1	spline2	spline3	spline4	spline5	spline6	spline7	spline8	spline9	spline10	spline11
date
2020-01-01	2.928244	0.166667	0.666667	0.166667	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2020-01-02	4.866145	0.150763	0.665604	0.183628	0.000006	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2020-01-03	4.425159	0.135904	0.662485	0.201563	0.000047	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

Radial basis functions (RBF)¶

In [18]:

# Radial basis functions
# ==============================================================================
rbf = RepeatingBasisFunction(
          n_periods   = 12,
          remainder   = 'drop',
          column      = 'day_of_year',
          input_range = (1, 365)
      )
rbf.fit(data)
rbf_month = rbf.transform(data)
rbf_month = pd.DataFrame(
                data    = rbf_month,
                index   = data.index,
                columns = [f"rbf_{i}" for i in range(splines_month.shape[1])]
            )
rbf_month.head(3)

# Radial basis functions # ============================================================================== rbf = RepeatingBasisFunction( n_periods = 12, remainder = 'drop', column = 'day_of_year', input_range = (1, 365) ) rbf.fit(data) rbf_month = rbf.transform(data) rbf_month = pd.DataFrame( data = rbf_month, index = data.index, columns = [f"rbf_{i}" for i in range(splines_month.shape[1])] ) rbf_month.head(3)

Out[18]:

	rbf_0	rbf_1	rbf_2	rbf_3	rbf_4	rbf_5	rbf_6	rbf_7	rbf_8	rbf_9	rbf_10	rbf_11
date
2020-01-01	1.000000	0.367879	0.018316	0.000123	1.125352e-07	1.388794e-11	2.319523e-16	1.388794e-11	1.125352e-07	0.000123	0.018316	0.367879
2020-01-02	0.998914	0.392526	0.020875	0.000150	1.463375e-07	1.929034e-11	3.441402e-16	9.976816e-12	8.635293e-08	0.000101	0.016035	0.344032
2020-01-03	0.995662	0.417914	0.023740	0.000183	1.898798e-07	2.673609e-11	5.094813e-16	7.151579e-12	6.611833e-08	0.000083	0.014009	0.321032

In [19]:

# Location of the maximum value of each rbf
# ==============================================================================
rbf_month.idxmax()

# Location of the maximum value of each rbf # ============================================================================== rbf_month.idxmax()

Out[19]:

rbf_0    2020-01-01
rbf_1    2020-01-31
rbf_2    2020-03-02
rbf_3    2020-04-01
rbf_4    2020-05-01
rbf_5    2020-06-01
rbf_6    2020-07-01
rbf_7    2020-07-31
rbf_8    2020-08-31
rbf_9    2020-09-30
rbf_10   2020-10-30
rbf_11   2020-11-30
dtype: datetime64[ns]

In [20]:

# Encoded data
# ==============================================================================
data_encoded_rbf = pd.concat([data, rbf_month], axis=1)
data_encoded_rbf = data_encoded_rbf.drop(columns=['day_of_year', 'month'])
data_encoded_rbf.head(3)

# Encoded data # ============================================================================== data_encoded_rbf = pd.concat([data, rbf_month], axis=1) data_encoded_rbf = data_encoded_rbf.drop(columns=['day_of_year', 'month']) data_encoded_rbf.head(3)

Out[20]:

	y	rbf_0	rbf_1	rbf_2	rbf_3	rbf_4	rbf_5	rbf_6	rbf_7	rbf_8	rbf_9	rbf_10	rbf_11
date
2020-01-01	2.928244	1.000000	0.367879	0.018316	0.000123	1.125352e-07	1.388794e-11	2.319523e-16	1.388794e-11	1.125352e-07	0.000123	0.018316	0.367879
2020-01-02	4.866145	0.998914	0.392526	0.020875	0.000150	1.463375e-07	1.929034e-11	3.441402e-16	9.976816e-12	8.635293e-08	0.000101	0.016035	0.344032
2020-01-03	4.425159	0.995662	0.417914	0.023740	0.000183	1.898798e-07	2.673609e-11	5.094813e-16	7.151579e-12	6.611833e-08	0.000083	0.014009	0.321032

Compare forecasting results¶

A non-informative lag is included so that the impact of cyclical features can be assessed without being obscured by the autoregressive component.

In [21]:

# Create forecaster
# ==============================================================================
forecaster = ForecasterAutoreg(
                 regressor = HistGradientBoostingRegressor(random_state=123),
                 lags      = [70]
             )

# Create forecaster # ============================================================================== forecaster = ForecasterAutoreg( regressor = HistGradientBoostingRegressor(random_state=123), lags = [70] )

In [22]:

# Train and validate a forecaster using each encoding method
# ==============================================================================
datasets = [data_encoded_oh, data_encoded_sin_cos, data_encoded_splines,
            data_encoded_rbf]
encoding_methods = ['one hot encoding', 'sine/cosine encoding', 'spline encoding',
                    'rbf encoding']

fig, ax = plt.subplots(figsize=(8, 4))
data_test['y'].plot(title="Time series", label="test", ax=ax)

for i, data_encoded in enumerate(datasets):

    metric, predictions = backtesting_forecaster(
                              forecaster         = forecaster,
                              y                  = data_encoded['y'],
                              exog               = data_encoded.drop(columns='y'),
                              initial_train_size = len(data_encoded.loc[:end_train]),
                              fixed_train_size   = False,
                              steps              = 365,
                              refit              = False,
                              metric             = 'mean_squared_error',
                              verbose            = False, # Change to True to see detailed information
                              show_progress      = False
                          )

    print(f"Backtest error using {encoding_methods[i]}: {metric:.2f}")
    predictions.plot(label=encoding_methods[i], ax=ax)
    ax.legend(labels=['test'] + encoding_methods);

# Train and validate a forecaster using each encoding method # ============================================================================== datasets = [data_encoded_oh, data_encoded_sin_cos, data_encoded_splines, data_encoded_rbf] encoding_methods = ['one hot encoding', 'sine/cosine encoding', 'spline encoding', 'rbf encoding'] fig, ax = plt.subplots(figsize=(8, 4)) data_test['y'].plot(title="Time series", label="test", ax=ax) for i, data_encoded in enumerate(datasets): metric, predictions = backtesting_forecaster( forecaster = forecaster, y = data_encoded['y'], exog = data_encoded.drop(columns='y'), initial_train_size = len(data_encoded.loc[:end_train]), fixed_train_size = False, steps = 365, refit = False, metric = 'mean_squared_error', verbose = False, # Change to True to see detailed information show_progress = False ) print(f"Backtest error using {encoding_methods[i]}: {metric:.2f}") predictions.plot(label=encoding_methods[i], ax=ax) ax.legend(labels=['test'] + encoding_methods);

Backtest error using one hot encoding: 1.10
Backtest error using sine/cosine encoding: 1.12
Backtest error using spline encoding: 0.75
Backtest error using rbf encoding: 0.74