Introduction to Time Series

A critical first step in any time series workflow is verifying data types. Time series analysis relies heavily on correctly formatted datetime variables and numeric measurements. If dates are stored as strings or numeric values are stored as text due to special symbols (such as "T" for trace precipitation or "M" for missing values), many operations—including plotting, resampling, and interpolation—will fail or produce misleading results.

In this dataset, the DATE column must be converted to a datetime object so Python can recognize the temporal ordering of observations. Likewise, temperature, precipitation, and wind variables should be numeric wherever possible.

For reference, Python’s standard built-in data types include:

Source: www.geeksforgeeks.org/python-data-types/

As you explore the dataset, keep the following guiding questions in mind:

To begin, load the 2006 Indianapolis weather dataset and preview the first few rows. This initial glance can reveal missing values, repeated timestamps, or unexpected reporting formats.

A preview of selected columns is shown below to illustrate the structure of the raw data:

Next, examine the size of the dataset and inspect the column names and data types. This information helps determine which variables are likely useful for daily weather analysis and whether any columns will require type conversion or cleaning.

Pay particular attention to daily weather variables such as:

Many of these variables are numeric and well-suited for analysis, while others, such as precipitation and snowfall, may be stored as objects due to special symbols or missing values.

Time series analysis requires a properly formatted datetime variable. Convert the DATE column using pd.to_datetime() and inspect the result.

Notice that multiple observations can occur within the same calendar day, indicating that the dataset contains sub-daily (hourly or event-based) records rather than exactly one row per day.

Finally, compare the number of unique calendar dates to the total number of rows. This step confirms whether the dataset represents a true daily time series or a higher-frequency dataset that will need to be aggregated.

In 2006, there are 365 unique calendar days in the dataset, but many more rows, confirming that multiple observations exist per day. This observation will directly inform how the data must be transformed before applying daily time series models.

Each year of weather data is stored in its own CSV file, following a consistent naming pattern:

You can view the available files on ANVIL using the following command:

Each file contains weather observations for a single year. Our goal is to stack these files together so that the resulting dataset represents a continuous timeline spanning multiple years.

Conceptually, this process is known as appending or stacking data: placing one dataset directly on top of another when they share the same columns. This idea is illustrated below:

As you work through this section, consider the following:

To streamline the stacking process, use the function provided below. This function reads each yearly file, adds a year column for tracking, and concatenates all files into a single DataFrame.

Use this function to load and combine all available years:

Once all years have been stacked together, examine the size of the combined dataset. This helps verify that the stacking process worked as expected.

Next, break the dataset down by year to see how many rows are recorded for each year:

This breakdown reveals whether some years contain more observations than others and provides early insight into data completeness across time.

The full dataset contains many columns, including hourly, daily, and monthly measurements. For the next stages of this project, we will focus on a smaller set of daily weather variables that are most relevant for time series analysis.

Use the code below to filter the dataset to a core set of columns and overwrite the existing DataFrame with this simplified version:

Before working with time series models, the dataset must be cleaned so that each row represents meaningful daily weather information. This step focuses on selecting relevant variables, removing unusable rows, and ensuring the time variable is stored in a format suitable for time-based analysis.

The variables of interest represent key daily weather conditions, including average, minimum, and maximum temperatures, precipitation, humidity, wind speed, and snowfall. These measurements form the foundation for identifying trends, seasonal patterns, and long-term changes in climate.

Because this project focuses on daily time series analysis, the dataset must satisfy two conditions:

Many time series operations in Python rely on the dataframe index rather than column values. One such operation is time-based interpolation, which fills in missing values using the spacing between dates.

If interpolation is attempted before setting the date as the index, Python cannot determine which column represents time. As a result, time-based methods will fail.

For example, attempting to interpolate temperature values without a datetime index produces an error:

The error typically indicates that time-weighted interpolation only works when the index is a DatetimeIndex. Without this structure, Python has no information about how observations are ordered in time.

To enable time-aware operations, the DATE column must first be converted to a datetime format and then set as the dataframe index.

Once DATE is set as the index, Python recognizes the dataset as time-ordered. Time-based interpolation can now be applied correctly, filling gaps using nearby dates along the timeline.

This approach preserves the structure of the time series and avoids introducing artificial jumps or distortions.

Before applying interpolation across the dataset, it is important to identify which columns are numeric. Time-based interpolation should only be applied to numeric variables such as temperature, humidity, and wind speed.

These columns represent continuous measurements that vary smoothly over time and are appropriate for interpolation.

With DATE set as the index and numeric columns identified, time-based interpolation can be applied across all numeric variables simultaneously.

This ensures that missing values are filled in a way that respects the temporal spacing between observations.

After time-based operations are complete, it is often useful to restore DATE as a regular column. This makes the dataframe easier to inspect, merge with other datasets, or visualize using plotting libraries.

Weather datasets often include trace values recorded as "T", representing very small amounts of precipitation or snowfall. These values are not numeric and must be addressed before modeling or statistical analysis.

Attempting to convert columns containing "T" directly to floats will result in errors. To resolve this, trace values are replaced with 0 and the columns are then converted to numeric types.

A final check confirms that no trace values remain in the dataset.

The daily average temperature variable is stored in Celsius. For interpretability and consistency with common U.S. weather reporting, the temperature is converted to Fahrenheit and stored as a new column. Keeping the original Celsius values ensures that no information is lost during the transformation.

Plotting daily average temperature from 2006 to 2024 provides a long-term view of climate behavior in Indianapolis. At this scale, individual daily fluctuations blend together, allowing broader patterns to stand out more clearly.

When viewed over many years, several features typically emerge:

This visualization helps distinguish long-term structure from short-term noise.

To better understand seasonal behavior, it is useful to focus on a single year. Examining daily average temperature for 2024 allows individual seasonal transitions to be observed more clearly.

At this resolution, patterns such as the following are easier to identify:

Anomalies also become more visible when zoomed in. Brief temperature drops near or below freezing in winter, sudden spikes during transitional months, and irregular spring or fall behavior stand out more clearly at the single-year level.

Time series visualization provides critical insight into how data behaves over time. Observing strong seasonality suggests the need for models that account for periodic patterns. Long-term trends motivate forecasting approaches that allow gradual change, while anomalies highlight periods that may require special attention.

By examining climate data at both long-term and short-term scales, the dataset begins to tell a story about how temperature evolves over time. This understanding lays the groundwork for the forecasting and modeling techniques introduced next.

Time-based features allow temporal patterns to be expressed explicitly in the data. Rather than expecting a model to infer seasonality solely from the order of observations, features such as month provide a clear signal about where each observation falls within the yearly cycle.

For climate data, month-based features are especially useful because they align closely with natural seasonal changes:

Encoding this information directly makes seasonal structure easier to visualize and model.

To capture seasonal information, the month is extracted from the DATE column and stored as a new variable. The month is represented as a string so it can be treated as a categorical feature in later analyses or models.

This new column assigns each observation a value from "1" through "12", corresponding to January through December.

Once the month feature is created, temperatures can be aggregated across all years to examine typical seasonal behavior. Averaging daily temperatures by month reveals long-term patterns that persist year after year.

This table summarizes the average temperature associated with each month, smoothing out daily and yearly fluctuations to highlight the overall seasonal cycle.

A line plot of average monthly temperatures provides a clear visual representation of seasonal trends. Unlike daily time series plots, this view emphasizes recurring patterns rather than short-term variability.

This visualization typically reveals a smooth seasonal curve:

Month-based features play an important role in time series forecasting. Strong seasonal structure suggests that future models should account for periodic behavior rather than treating observations as independent over time.

By explicitly encoding seasonal information and summarizing long-term monthly trends, the dataset becomes better suited for forecasting methods that rely on seasonality, such as SARIMAX models or machine learning approaches that incorporate time-based features.

This step bridges exploratory visualization and formal modeling, ensuring that temporal patterns observed in the data are captured directly in the features used for prediction.