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Methods of Data Preprocessing for Time Series Prediction in Python

by Kaison Francis

Time series forecasting is the process of making predictions by analyzing the past. However, to perfectly develop quality, dependable forecasting models, you must preprocess a time stream of data.

And for quality reprocessing, you’ll have to eliminate all the noises, handle missing values, and convert any data you get to match your forecasting model’s hypothesis. This article discusses some of the best Python time series forecasting methods and how you can apply them.

Dealing with Missing Data

The accuracy of forecasting models based on time series can be dramatically impacted when values are missing. Filling in missing values with the help of interpolation techniques like linear interpolation or forward/backward filling is a standard approach that’s often taken.

To deal with missing values in time series data, you can use Python libraries such as Pandas provide useful functions such as interpolate () and fillna ().

Removing the Odds or Outliers

Time series data can be distorted by outliers, making predictions difficult. Preprocessing relies heavily on spotting anomalies and getting rid of them.

It is possible to identify outliers with the help of z-scores and boxplots or statistical tests like the Grubbs’ test. When abnormal values are found, they can be changed or eliminated for better ones.

Resampling and Aggregating

Recording time series data at irregular intervals might be difficult for prediction algorithms. By resampling, data can be transformed from one frequency to another, from daily to monthly or quarterly.

Noise can be reduced, and more reliable visualization of the underlying patterns can be obtained by consolidating the data using sums, averages, or other comparable/statistical measures.

Dealing with Trends and Seasons

Seasonal variations and long-lasting trends in time series data are common sources of error in predicting. Fortunately, you can eliminate seasonality and trends from your data using various statistical methods, including differencing, seasonal decomposition, and detrending.

Seasonal decomposition divides your data into respective, trend, or residual components, whereas differencing uses the variation between consecutive observations to filter out trends.

Scaling and Normalization 

Time series data with several scales or units requires careful application of scaling and normalizing methods.

To keep the forecasting models from being overrun by dominating features, it is possible to apply techniques like Min-Max scaling and Standardization that guarantee that every variable maintains the same scale. Scikit-learn and other libraries have helpful normalization and scaling tools like MinMaxScaler and StandardScaler.

Managing the Non-Stationary Situation

Many forecasting models assume that the fundamental time series data remains stationary. This means that the models presume that the data’s statistical features, like mean and variance, do not change over time. Changing the series into a stationary format is essential if the data demonstrate non-stationarity.

You can also achieve stationarity by employing techniques such as logarithmic transformations, differencing, or the application of mathematical functions such as Box-Cox transformations.

Managing a Biased or Skewed Data 

Time series data can benefit from power or logarithmic transformations if the data is extremely skewed and has a non-normal distribution. Some forecasting models, especially those that presume normalcy, perform better when the data has been transformed in this way.

Separating into Test and Practice Groups

Separating the time series data into a training set and a test set is a necessary step that must be completed before creating the forecasting models.

Both the training material and the test set will be utilized in the process of training and evaluating the models. When dividing up data from a time series, it is essential to do so in a way that maintains the observations in the correct sequence according to their chronological progression.


Preprocessing techniques are necessary to prepare accurate and dependable time series data. You can ensure your data is adequately prepared for your forecasting goals if you deal with missing values, resample and aggregate, remove outliers, and address trends and seasonality. Better still, ensure you handle all the skewed data and scale or normalize information of different units.

The good news is that you can implement Python time series forecasting, which comes with a number of built-in libraries, such as Pandas and Scikit-learn. These libraries give a variety of straightforward processes and functions for putting these preprocessing techniques into effect.

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