9 Regression for Time Series Forecasting (with `sklearn`)

Let’s explore an example of how to use regression to perform trend analysis with time series data.

9.1 Data Loading

As an example time series dataset, let’s consider this dataset of U.S. population over time, from the Federal Reserve Economic Data (FRED).

Fetching the data, going back as far as possible:

from pandas_datareader import get_data_fred

DATASET_NAME = "POPTHM"
df = get_data_fred(DATASET_NAME, start="1900-01-01")
print(len(df))
df

	POPTHM
DATE
1959-01-01	175818
1959-02-01	176044
1959-03-01	176274
...	...
2025-12-01	342495
2026-01-01	342540
2026-02-01	342581

806 rows × 1 columns

Data Source

Here is some more information about the “POPTHM” dataset:

“Population includes resident population plus armed forces overseas. The monthly estimate is the average of estimates for the first of the month and the first of the following month.”

The data is expressed in “Thousands”, and is “Not Seasonally Adjusted”.

Wrangling the data, including renaming columns and converting the date index to be datetime-aware, may make it easier for us to work with this data:

from pandas import to_datetime

df.rename(columns={DATASET_NAME: "population"}, inplace=True)
df.index.name = "date"
df.index = to_datetime(df.index)
df

	population
date
1959-01-01	175818
1959-02-01	176044
1959-03-01	176274
...	...
2025-12-01	342495
2026-01-01	342540
2026-02-01	342581

806 rows × 1 columns

9.2 Data Exploration

Exploring trends:

import plotly.express as px

px.scatter(df, y="population", title="US Population (Monthly) vs Trend",
            labels={"population":"US Population (thousands)", "value":""},
            trendline="ols", trendline_color_override="red", height=350,
)

Looks like a possible linear trend. Let’s perform a more formal regression analysis.

9.3 Data Encoding

Because we need numeric features to perform a regression, we convert the dates to a linear time step of integers (after sorting the data first for good measure):

df.sort_values(by="date", ascending=True, inplace=True)

df["time_step"] = range(1, len(df) + 1)
df

	population	time_step
date
1959-01-01	175818	1
1959-02-01	176044	2
1959-03-01	176274	3
...	...	...
2025-12-01	342495	804
2026-01-01	342540	805
2026-02-01	342581	806

806 rows × 2 columns

We will use the numeric time step as our input variable (x), to predict the population (y).

9.4 Data Splitting

9.4.1 X/Y Split

Identifying dependent and independent variables:

#x = df[["date"]] # we need numbers not strings
x = df[["time_step"]]

y = df["population"]

print("X:", x.shape)
print("Y:", y.shape)

X: (806, 1)
Y: (806,)

9.4.2 Train/Test Split

Splitting data sequentially, where earlier data is used in training, and recent data is used for testing:

print(len(df))

training_size = round(len(df) * .8)
print(training_size)

x_train = x.iloc[:training_size] # slice all before
y_train = y.iloc[:training_size] # slice all before

x_test = x.iloc[training_size:] # slice all after
y_test = y.iloc[training_size:] # slice all after
print("TRAIN:", x_train.shape)
print("TEST:", x_test.shape)

806
645
TRAIN: (645, 1)
TEST: (161, 1)

9.5 Model Selection and Training

Training a linear regression model on the training data:

from sklearn.linear_model import LinearRegression

model = LinearRegression()

model.fit(x_train, y_train)

LinearRegression()

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After training, we have access to the learned weights, as well as the line of best fit (i.e. the trend line):

print("COEFS:", model.coef_)
print("INTERCEPT:", model.intercept_)
print("------------------")
print(f"y = {model.coef_[0].round(3)}x + {model.intercept_.round(3)}")

COEFS: [213.54325193]
INTERCEPT: 174434.39939332657
------------------
y = 213.543x + 174434.399

In this case, we interpret the line of best fit to observe how much the population is expected to grow on average per time step, as well as the population trend value at the earliest time step.

Note

Remember in this dataset the population is expressed in thousands.

9.6 Model Prediction and Evaluation

We use the trained model to make predictions on the test set, and then calculate regression metrics to see how well the model is doing:

from sklearn.metrics import mean_squared_error, r2_score

y_pred = model.predict(x_test)

mse = mean_squared_error(y_test, y_pred)
print("MSE:", round(mse, 4))

r2 = r2_score(y_test, y_pred)
print("R2:", round(r2, 4))

MSE: 6129807.2072
R2: 0.8949

A very high r-squared value represents a strong linear trend.

Plotting predictions (trend line):

df["prediction"] = model.predict(df[["time_step"]])
df["error"] = df["population"] - df["prediction"]

px.line(df, y=["population", "prediction"], height=350,
    title="US Population (Monthly) vs Regression Predictions (Trend)",
    labels={"value":""}
)

9.7 Forecasting

Assembling a dataset of future dates and time steps (which we can use as inputs to the trained model to make predictions about the future):

from pandas import date_range, DateOffset, DataFrame

last_time_step = df['time_step'].iloc[-1]
last_date = df.index[-1]
next_date = last_date + DateOffset(months=1)

FUTURE_MONTHS = 36
# frequency of "M" for end of month, "MS" for beginning of month
future_dates = date_range(start=next_date, periods=FUTURE_MONTHS, freq='MS')
future_time_steps = range(last_time_step + 1, last_time_step + FUTURE_MONTHS + 1)

df_future = DataFrame({'time_step': future_time_steps}, index=future_dates)
df_future.index.name = "date"
df_future

	time_step
date
2026-03-01	807
2026-04-01	808
2026-05-01	809
...	...
2028-12-01	840
2029-01-01	841
2029-02-01	842

36 rows × 1 columns

Predicting future values:

df_future["prediction"] = model.predict(df_future[["time_step"]])
df_future

	time_step	prediction
date
2026-03-01	807	346763.803703
2026-04-01	808	346977.346955
2026-05-01	809	347190.890207
...	...	...
2028-12-01	840	353810.731016
2029-01-01	841	354024.274268
2029-02-01	842	354237.817520

36 rows × 2 columns

Concatenating historical data with future data:

from pandas import concat

chart_df = concat([df, df_future])
chart_df

	population	time_step	prediction	error
date
1959-01-01	175818.0	1	174647.942645	1170.057355
1959-02-01	176044.0	2	174861.485897	1182.514103
1959-03-01	176274.0	3	175075.029149	1198.970851
...	...	...	...	...
2028-12-01	NaN	840	353810.731016	NaN
2029-01-01	NaN	841	354024.274268	NaN
2029-02-01	NaN	842	354237.817520	NaN

842 rows × 4 columns

Note

The population and error values for future dates are null, because we don’t know them yet. Although we are able to make predictions about these values, based on historical trends.

Plotting trend vs actual, with future predictions:

px.line(chart_df[-180:], y=["population", "prediction"], height=350,
    title="US Population (Monthly) vs Regression Predictions (Trend)",
    labels={"value":""}
)