## Tags: ARIMA

### Air quality in Indian cities

Share Tweet Seasonal air pollution in India The motivation for this blog post was a conference paper I recently heard that analysed five years of daily pollution data in an India city...

### Forecast double seasonal time series with multiple linear regression in R

I will continue in describing forecast methods, which are suitable to seasonal (or multi-seasonal) time series. In the previous post smart meter data of electricity consumption were i...

### 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 clas...

### Vincent, one way bumpiness might be interpreted...

from a practitioner's perspective, is that it is a measure of noise, a detector of outliers that may show up as unaccounted-for noise, from the way, say, a process is producing the da...

### 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 B...

### Introduction to Forecasting with ARIMA in R

ARIMA models are a popular and flexible class of forecasting model that utilize historical information to make predictions. In this tutorial, we walk through an example of examining t...

### How to Grid Search ARIMA Model Hyperparameters with Python

The ARIMA model for time series analysis and forecasting can be tricky to configure. There are 3 parameters that require estimation by iterative trial and error from reviewing diagnos...

### Simulating from a specified seasonal ARIMA model

Share Tweet From my email today You use an illustration of a seasonal arima model: ARIMA(1,1,1)(1,1,1)4 I would like to simulate data from this process then fit a model… but I am unab...

### How to Make Manual Predictions for ARIMA Models with Python

The autoregression integrated moving average model or ARIMA model can seem intimidating to beginners. A good way to pull back the curtain in the method is to to use a trained model to...

### Understand Time Series Forecast Uncertainty Using Confidence Intervals with Python

Time series forecast models can both make predictions and provide a confidence interval for those predictions. Confidence intervals provide an upper and lower expectation for the real...

### How to Save an ARIMA Time Series Forecasting Model in Python

The Autoregressive Integrated Moving Average Model, or ARIMA, is a popular linear model for time series analysis and forecasting. The statsmodels library provides an implementation of...

### How to Make Out-of-Sample Forecasts with ARIMA in Python

Making out-of-sample forecasts can be confusing when getting started with time series data. The statsmodels Python API provides functions for performing one-step and multi-step out-of...

### Sensitivity Analysis of History Size to Forecast Skill with ARIMA in Python

How much history is required for a time series forecast model? This is a problem-specific question that we can investigate by designing an experiment. In this tutorial, you will disco...

### How to Tune ARIMA Parameters in Python

There are many parameters to consider when configuring an ARIMA model with Statsmodels in Python. In this tutorial, we take a look at a few key parameters (other than the order parame...

### A useful forecast combination benchmark

Share Tweet Forecasting benchmarks are very important when testing new forecasting methods, to see how well they perform against some simple alternatives. Every week I get sent papers...