Time series analysis plays an important role in numerous applications. There are limited univariate time series forecasting methods and ARIMA is one of the leading methods in the domain.
Time series analysis plays an important role in numerous applications
There are limited univariate time series forecasting methods and ARIMA is one of the leading methods in the domain
PSF, a possible alternative for ARIMA method for seasonal univariate time series forecasting
This post describes and demonstrates the PSF method and its R package
Challenges in univariate time series analysis:
Time series analysis plays an important role in numerous applications such as healthcare, economics, finance, environment, climate, agriculture, research, energy, and many more. There are several aspects of time series analysis, which includes predictions, univariate forecasts, missing value imputations, outlier detections, time series transformation, and cleaning, etc. In most of the applications, the final goal is to achieve an accurate prediction or forecasting results. Out of these, forecasting a univariate time series is one of the challenging tasks, since such a process needs to understand, recreate, and extrapolate the patterns available in the targeted time series itself. Whereas, in the multivariate prediction process, there can be the availability of several variables with which predicting process usually becomes easier. This is the reason that there are very few methodologies are available for univariate time series forecasting than the multivariate prediction ones.
The ‘Forecast’ package in R is a boon for time series analysis, which provides several features and benchmarks including ARIMA and ETS methods and corresponding user-friendly interface. In this blog, I am introducing and demonstrating a very interesting R package for time series forecasting, which can be an excellent alternative and replacement for the benchmarked forecasting methods, especially for the time series with seasonal characteristics.
Yes, it is the ‘Pattern Sequence-based forecasting algorithm’, popularly known as ‘PSF’. It is a successful forecasting method based on the assumption that there exist pattern sequences in the time series. It was proposed in the year 2011 and published here.
Applying the ARIMA model to forecast time series dataThe notion of stationarity of a series is important for applying statistical forecasting models since.
In this paper, a time series analysis to predict the number of deaths in the United States starting from August 1st — August 21st and August 1st — November 1st is modeled and studied. The time series model that was selected to make the prediction is called Auto Regressive Integrated Moving Average (ARIMA) model.
Time Series analysis is a standard machine learning problem. We'll perform Time Series Analysis in R. It is a hands-on project where we will use time-series energy data. We will understand how techniques such as time-based indexing, resampling, and rolling window can help us explore electricity demand variations and renewable energy supply over time.
This article is just showing the fundamentals on how to do the analysis and not tackling a problem that is using the ARIMA model and the data that I used still stationary data.
Variation in statistical characteristics of univariate time series can have a profound effect on the characteristics of missing observations and, therefore, the accuracy of different imputation methods.