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## A Gentle Introduction to the Box-Jenkins Method for Time Series Forecasting

The Autoregressive Integrated Moving Average Model, or ARIMA for short is a standard statistical model for time series forecast and analysis. Along with its development, the authors Box and Jenkins also suggest a process for identifying, estimating, and checking models for a specific time series dataset. This process is now referred to as the Box-Jenkins […]

## How to Model Residual Errors to Correct Time Series Forecasts with Python

The residual errors from forecasts on a time series provide another source of information that we can model. Residual errors themselves form a time series that can have temporal structure. A simple autoregression model of this structure can be used to predict the forecast error, which in turn can be used to correct forecasts. This […]

## How to Create an ARIMA Model for Time Series Forecasting with Python

A popular and widely used statistical method for time series forecasting is the ARIMA model. ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average. It is a class of model that captures a suite of different standard temporal structures in time series data. In this tutorial, you will discover how to develop an […]

## How to Visualize Time Series Residual Forecast Errors with Python

Forecast errors on time series regression problems are called residuals or residual errors. Careful exploration of residual errors on your time series prediction problem can tell you a lot about your forecast model and even suggest improvements. In this tutorial, you will discover how to visualize residual errors from time series forecasts. After completing this […]

## Time Series Data Visualization with Python

6 Ways to Plot Your Time Series Data with Python Time series lends itself naturally to visualization. Line plots of observations over time are popular, but there is a suite of other plots that you can use to learn more about your problem. The more you learn about your data, the more likely you are […]

## Autoregression Models for Time Series Forecasting With Python

Autoregression is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. It is a very simple idea that can result in accurate forecasts on a range of time series problems. In this tutorial, you will discover how to […]

## How to Check if Time Series Data is Stationary with Python

Time series is different from more traditional classification and regression predictive modeling problems. The temporal structure adds an order to the observations. This imposed order means that important assumptions about the consistency of those observations needs to be handled specifically. For example, when modeling, there are assumptions that the summary statistics of observations are consistent. […]

## Moving Average Smoothing for Data Preparation, Feature Engineering, and Time Series Forecasting with Python

Moving average smoothing is a naive and effective technique in time series forecasting. It can be used for data preparation, feature engineering, and even directly for making predictions. In this tutorial, you will discover how to use moving average smoothing for time series forecasting with Python. After completing this tutorial, you will know: How moving […]

## How to Make Baseline Predictions for Time Series Forecasting with Python

Establishing a baseline is essential on any time series forecasting problem. A baseline in performance gives you an idea of how well all other models will actually perform on your problem. In this tutorial, you will discover how to develop a persistence forecast that you can use to calculate a baseline level of performance on […]