To analyze time series data in Python, first put observations on a correctly parsed, chronologically sorted date index and establish what each row represents. Then inspect trend and seasonality, check whether the series is stationary, and—if forecasting—evaluate predictions on later observations that were kept out of model fitting. These five adaptable scripts cover those steps with pandas, statsmodels, scikit-learn, and Prophet. They are examples, not tested or production-ready software; confirm current package APIs and adapt them to your data.
1. Parse timestamps, sort observations, and resample
Before modeling, make the date column usable as a time index and check the observation cadence. Resampling can group observations into a regular interval, but it cannot determine what missing periods mean in your data. The right aggregation depends on the measurement: summing transactions by day differs from averaging a sensor reading.
import pandas as pd
# Example input columns: timestamp, value
df = pd.read_csv("observations.csv")
df["timestamp"] = pd.to_datetime(df["timestamp"], errors="coerce")
df["value"] = pd.to_numeric(df["value"], errors="coerce")
df = df.dropna(subset=["timestamp", "value"])
df = df.sort_values("timestamp").set_index("timestamp")
# Example only: aggregate a numeric measure to daily sums.
daily = df["value"].resample("D").sum()
print(daily.index.freq)
print(daily.isna().sum())Change "D" and sum() to match the source cadence and meaning of the data; for example, an average may be more appropriate for repeated measurements. Resampling bins observations and applies an aggregation rule. It does not automatically establish whether an empty bin means zero activity, an unrecorded value, or a missing data collection period. Decide how to handle those cases before filling gaps or interpreting later results. See the pandas time-series guide for date and frequency functionality.
2. Decompose the series into trend, seasonality, and residuals
Decomposition helps answer whether a series appears to combine a longer-term movement with a recurring pattern. The residual is what remains after the chosen trend and seasonal components are accounted for; it is not automatically noise or proof that the model is adequate. Statsmodels documents decomposition methods and examples, including multi-seasonal approaches.
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from statsmodels.tsa.seasonal import seasonal_decompose
# Use a regular series and set period to the number of observations
# in one expected seasonal cycle.
result = seasonal_decompose(daily, model="additive", period=7)
result.plot()This illustrative weekly period assumes daily observations with a weekly cycle; it is not a universal default. Choose a period that matches the cadence and a plausible seasonal cycle, and ensure the history covers enough cycles to make the pattern interpretable. An additive decomposition is suited to a pattern whose seasonal swings are roughly constant in size; multiplicative decomposition represents seasonal variation that changes in proportion to the series level and requires suitable positive values. If seasonality is not stable or there are several seasonal cycles, consider an approach appropriate to that structure rather than forcing a single-period decomposition. Statsmodels’ examples cover decomposition alongside other time-series methods.
3. Check stationarity and consider detrending
Stationarity diagnostics help assess whether a series’ statistical behavior changes over time, a consideration for some time-series models. The Augmented Dickey–Fuller (ADF) and KPSS tests use different null hypotheses, so their results should be interpreted together with a plot, domain context, and the intended model—not as a universal pass/fail certification.
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from statsmodels.tsa.stattools import adfuller, kpss
series = daily.dropna()
adf_stat, adf_p, *_ = adfuller(series)
kpss_stat, kpss_p, *_ = kpss(series, regression="c", nlags="auto")
print("ADF statistic and p-value:", adf_stat, adf_p)
print("KPSS statistic and p-value:", kpss_stat, kpss_p)In broad terms, ADF tests a unit-root null, while KPSS tests a stationarity null under the specified regression form. A small p-value therefore points in different directions for the two tests. Trends, structural changes, seasonal effects, missing observations, and the chosen test settings can all affect interpretation. If the series is nonstationary for a model that expects stationarity, investigate transformations or detrending and reassess; do not assume a test result alone identifies the right remedy. Statsmodels lists stationarity and detrending examples in its examples and exposes time-series tools in its API reference.
4. Turn past observations into lagged forecasting features
A feature-based forecast treats time-series prediction as supervised learning: past values become input columns, and a later value is the target. The split must preserve chronology. Randomly shuffling observations can let information from the future influence training or validation, producing an evaluation that does not represent forecasting into the future.
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import pandas as pd
from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.metrics import mean_absolute_error
# daily is a regular, ordered pandas Series.
data = pd.DataFrame({"y": daily})
data["lag_1"] = data["y"].shift(1)
data["lag_7"] = data["y"].shift(7)
data["day_of_week"] = data.index.dayofweek
supervised = data.dropna()
# Hold the latest observations out; do not shuffle.
split = int(len(supervised) * 0.8)
train, test = supervised.iloc[:split], supervised.iloc[split:]
features = ["lag_1", "lag_7", "day_of_week"]
model = HistGradientBoostingRegressor()
model.fit(train[features], train["y"])
prediction = model.predict(test[features])
print("MAE:", mean_absolute_error(test["y"], prediction))The example predicts each held-out row using lag values already observed in the series. For a multi-step forecast made all at once, future lag values are not yet known: decide whether to predict recursively, use a direct multi-horizon setup, or otherwise construct features using only information available at each forecast origin. Calendar features also need to be available for the forecast dates. Compare against a simple baseline and use a time-aware evaluation design suited to the deployment horizon; a single chronological holdout is only one evaluation choice. Scikit-learn’s examples include lagged-feature forecasting and time-related feature engineering.
5. Fit and inspect a Prophet forecast
Prophet provides a forecasting workflow based on a dataframe with a datestamp column named ds and a numeric target named y. Its additive model can represent nonlinear trend, yearly, weekly, and daily seasonality, as well as holiday effects. The project says it works best when seasonal effects are strong and several seasons of historical data are available; suitability depends on the actual series and forecast task.
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import pandas as pd
from prophet import Prophet
# source contains timestamp and value columns.
prophet_data = source.rename(
columns={"timestamp": "ds", "value": "y"}
)[["ds", "y"]].copy()
prophet_data["ds"] = pd.to_datetime(prophet_data["ds"])
prophet_data["y"] = pd.to_numeric(prophet_data["y"], errors="coerce")
prophet_data = prophet_data.dropna().sort_values("ds")
model = Prophet()
model.fit(prophet_data)
future = model.make_future_dataframe(periods=30, freq="D")
forecast = model.predict(future)
model.plot(forecast)
model.plot_components(forecast)Here, periods=30 requests 30 future dates and freq="D" sets a daily cadence; change both to fit the data and the forecast horizon. The plotted forecast and component plots help inspect the fitted trend and seasonal contributions, but visual fit on historical observations is not a substitute for evaluation on dates excluded from fitting. For a forecast assessment, reserve later observations before fitting and compare predictions with their actual values. Follow the Prophet Quick Start for the documented input and workflow, and consult the Prophet project README for project guidance.
How to choose a time-series workflow
These scripts answer different questions and are not interchangeable model contests. Statsmodels documents classical approaches such as ARIMA, exponential smoothing, decomposition, and stationarity checks; scikit-learn supports forecasts built from engineered features; Prophet offers a seasonal additive forecasting workflow. Select based on the structure and purpose of the data, then judge forecasts using a time-ordered evaluation rather than assuming any method is more accurate by default.
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- Cadence and history: Identify whether observations are hourly, daily, monthly, or irregular, and how many complete seasonal cycles are present. A period or frequency must correspond to the observations actually available.
- Recurring patterns: If a repeating cycle is evident, decomposition can help expose it, while a forecast method must be configured or engineered to represent it.
- Feature design: Lagged-feature models require deliberate decisions about which historical values and calendar attributes are known at prediction time.
- Interpretability: Decomposition provides explicit trend, seasonal, and residual views; a predictive model’s accuracy alone may not explain the series’ structure.
- Evaluation: Keep future observations out of training, use a horizon that matches the real forecast need, and compare methods only under a common time-aware validation setup.
For broader method details, consult the statsmodels examples, statsmodels API reference, and the statsmodels time-series documentation.
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