Real-world time series forecasting is challenging for a whole host of reasons not limited to problem features such as having multiple input variables, the requirement to predict multiple time steps, and the need to perform the same type of prediction for multiple physical sites. In this post, you will discover a standardized yet complex time […]

# Archive | Time Series

## How to Convert a Time Series to a Supervised Learning Problem in Python

Machine learning methods like deep learning can be used for time series forecasting. Before machine learning can be used, time series forecasting problems must be re-framed as supervised learning problems. From a sequence to pairs of input and output sequences. In this tutorial, you will discover how to transform univariate and multivariate time series forecasting […]

## Seasonal Persistence Forecasting With Python

It is common to use persistence or naive forecasts as a first-cut forecast on time series problems. A better first-cut forecast on time series data with a seasonal component is to persist the observation for the same time in the previous season. This is called seasonal persistence. In this tutorial, you will discover how to […]

## 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 parameter) that you may be curious about. Specifically, after completing this tutorial, you will know: How to suppress noisy output from the underlying mathematical […]

## Simple Time Series Forecasting Models to Test So That You Don’t Fool Yourself

It is important to establish a strong baseline of performance on a time series forecasting problem and to not fool yourself into thinking that sophisticated methods are skillful, when in fact they are not. This requires that you evaluate a suite of standard naive, or simple, time series forecasting models to get an idea of […]

## Feature Selection for Time Series Forecasting with Python

The use of machine learning methods on time series data requires feature engineering. A univariate time series dataset is only comprised of a sequence of observations. These must be transformed into input and output features in order to use supervised learning algorithms. The problem is that there is little limit to the type and number […]

## 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 discover the effect that history size has on the skill of an ARIMA forecast model in Python. Specifically, in this tutorial, you will: Load a standard […]

## 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-sample forecasts. In this tutorial, you will clear up any confusion you have about making out-of-sample forecasts with time series data in Python. After completing this tutorial, you will know: How […]

## Time Series Forecasting with Python 7-Day Mini-Course

From Developer to Time Series Forecaster in 7 Days. Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling time series forecasting projects using Python in 7 days. This is a big and important post. […]

## 4 Strategies for Multi-Step Time Series Forecasting

Time series forecasting is typically discussed where only a one-step prediction is required. What about when you need to predict multiple time steps into the future? Predicting multiple time steps into the future is called multi-step time series forecasting. There are four main strategies that you can use for multi-step forecasting. In this post, you […]