# Introduction
Working with time series data involves a fixed set of tasks. Raw data arrives at irregular intervals and requires resampling. The dramatic spikes need to be identified before they distort any downstream analysis. Seasonal trends and patterns need to be separated from the noise. And when you have a lot of threads, understanding how they relate to each other takes more than a quick visual scan.
These five Python scripts handle these common time series tasks. They are designed to work with standard CSV or Excel inputs, produce clean output, and are straightforward to prepare for different datasets.
You can find all the scripts on GitHub.
# 1. Resampling and Interpolation of Irregular Time Series
// Pain Point
Real-world time series data rarely arrive at uniform intervals. Sensor readings, transaction logs, and event streams have gaps, duplicates, and inconsistent timestamps. Before any meaningful analysis, the data needs to be consistent with a consistent frequency.
// What the Script Does
It takes a CSV or Excel file with a datetime column and one or more value columns, resamples at a frequency you specify, and uses functions to combine each column. It fills in or flags blanks and writes a clean output file with a summary of what was changed.
// How It Works
The script parses the date column as the pandassets it as an index, and uses it resample() with adjustable frequency bands. Aggregation methods for each column are defined in the configuration, so the temperature column can use the mean while the sales column uses the sum. Missing intervals after resampling are handled by padding, translation, or implicit NaN flagging depending on your setting. The gap report lists all intervals where data was initially missing.
⏩ Get the time series resampler script
# 2. Anomaly Detection in Time Series Data
// Pain Point
A single confusing spike or decline in a time series can shake the average, break down models, and mask true trends. Identifying these points manually by scanning plots or raw values is not possible for any reasonable data volume.
// What the Script Does
It scans one or more numeric columns in a time series file and flags a data point that falls outside the expected limits using a choice of three detection methods: z-score, interquartile range (IQR), or rolling statistics. It outputs an annotated file with error flags and a separate summary report.
// How It Works
The z-score method flags when the measured value exceeds an adjustable threshold (default ±3). The interquartile range (IQR) method flags indicators outside of 1.5× the interquartile range. The fold method calculates the moving average and standard deviation over an adjustable window and flags points that deviate significantly from the local context. This is useful for series with strong trends or seasonality. All three can be run together; the output column records which method flagged each point. Optional --plot the marker saves a chart for each column with the puzzles highlighted.
⏩ Get a bug detector script
# 3. Splitting the Series into Trend, Seasonal, and Residual
// Pain Point
A time series is usually a combination of several components: a long-term trend, a repeating seasonal pattern, and anomalous residual noise. Analyzing the series as a whole makes it difficult to understand any one part clearly.
// What the Script Does
It uses classical time series decomposition in the numerical column, separating the observed series into trend, seasonal, and residual components. It supports both additive and iterative decomposition models. Exports each component as a column in the output file and saves the multi-panel chart.
// How It Works
The script is running statsmodels.tsa.seasonal.seasonal_decompose() in the target column after resampling to a constant frequency if required. The decay time is adjustable. The additive decay fits a series where the seasonal variation is nearly constant in magnitude; a series of many suits when measuring the level of trend. The output Excel file contains the original series next to the extracted three parts. The saved chart shows all the stacked panels.
⏩ Get the time series decomposition script
# 4. Forecasting with Seasonal AutoRegressive Integrated Moving Average
// Pain Point
Generating a weather forecast from a time series often involves model selection, parameter tuning, and validation steps that require statistical knowledge to be correct. Setting this up from scratch each time is time-consuming, and doing it informally produces predictions that are difficult to trust or reproduce.
// What the Script Does
It fits a seasonal automatic aggregate moving average (SARIMA) model to the time series column, generates a forecast for an adjustable number of periods, and writes the results to an output file including forecast values, confidence intervals, and basic accuracy metrics over a specified validation period. It automatically selects model parameters using the Akaike information criterion (AIC) for minimization.
// How It Works
The script is running statsmodels.tsa.statespace.sarimax.SARIMAX for measuring the model. When --auto-order set, performs a lightweight grid search over an adjustable ARIMA range and seasonal parameters, selecting the combination with the lowest AIC. The series is divided into a training set and a test set which is fixed as the number of times. Accuracy is reported on the test set using the mean absolute error (MAE) and root mean square error (RMSE) before the final model is re-fitted to the full series to generate the prior forecast. Results include point prediction and 95% confidence intervals. A forecast chart is saved showing the historical series, the test period versus the forecasts, and the previous forecast with confidence bands.
⏩ Get the SARIMA forecast text
# 5. Comparing Multiple Time Series
// Pain Point
When you're working with multiple related time series — different products, regions, sensors, or metrics — understanding how they go together requires more than looking at them on the same chart. Correlation analysis, lag relationships, and correlated summary statistics all require a computer, and doing this for many pairs of series quickly becomes difficult.
// What the Script Does
It takes a file with multiple time series columns, aligns them to a common frequency, and produces a multi-tab comparison report that includes pairwise correlations, lag analysis (cross-correlation up to adjustable lags), and a statistical table of joint statistics. Charts are made of related top pairs.
// How It Works
The script uses pandas to align all columns in a shared datetime index after resampling. Pairwise Pearson and Spearman correlations are calculated and recorded in the correlation matrix tab. The cross-correlation is computed for each pair up to an adjustable height, which identifies the speed at which each pair peaks, which is useful for finding the lead/back relationship. The summary tab includes the mean, standard deviation, minimum, threshold, and trend direction (positive/negative slope from linear fit) for each series. The top five most correlated pairs each get a dual axis line chart in the dedicated charts tab.
⏩ Get a multithreaded comparison script
# Wrapping up
These five documents cover the main tasks involved in working with time series data. They are designed to be used independently or sequentially: resample first, find anomalies, decay, predict, and then compare across the series.
To get started, first download the script you plan to use and install all the dependencies listed in its README file. Next, update the configuration section at the top of the script to match your specific data and column names. Before running it on your full dataset, test the script on a small sample to make sure the output is correct. Once you're satisfied with the results, you can schedule or integrate it into your existing data line.
Happy analysis!
Count Priya C is an engineer and technical writer from India. He loves working at the intersection of mathematics, programming, data science, and content creation. His areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, he works to learn and share his knowledge with the engineering community by authoring tutorials, how-to guides, ideas, and more. Bala also creates engaging resource overviews and code tutorials.
