Discovering the Potential of ARIMA: Forecasting Future Trends with Time Series Analysis

 

Discovering the Potential of ARIMA: Forecasting Future Trends with Time Series Analysis



There are two prominent methods of time series prediction: univariate and multivariate.

·       Univariate uses only the previous values in the time series to predict future values.

·       Multivariate also uses external variables in addition to the series of values to create the forecast.

The ARIMA model predicts a given time series based on its own past values. It can be used for any nonseasonal series of numbers that exhibits patterns and is not a series of random events. For example, sales data from a clothing store would be a time series because it was collected over a period of time. One of the key characteristics is the data is collected over a series of constant, regular intervals. A modified version can be created to model predictions over multiple seasons.

The ARIMA model is becoming a popular tool for data scientists to employ for forecasting future demand, such as sales forecasts, manufacturing plans or stock prices. In forecasting stock prices, for example, the model reflects the differences between the values in a series rather than measuring the actual values.

Before we dig right into ARIMA’s formal mathematical definition, let me introduce you to the concept of stationarity. Stationarity simply means observations that do not depend on time. For data that depends on time (eg. seasonal rainfall), the stationarity condition may not hold as different timing will yield different values for these observations.

Another important concept to understanding ARIMA is autocorrelation. How does it differ][ from the typical correlation? First of all, correlation relates two different sets of observations (eg. between housing prices and the number of available public amenities) while autocorrelation relates the same set of observation but across different timing (eg. between rainfall in the summer versus that in the fall).

 

ARIMA in Time Series Analysis

An autoregressive integrated moving average – ARIMA model is a generalization of a simple autoregressive moving average – ARMA model. Both of these models are used to forecast or predict future points in the time-series data. ARIMA is a form of regression analysis that indicates the strength of a dependent variable relative to other changing variables.

 

The final objective of the model is to predict future time series movement by examining the differences between values in the series instead of through actual values. ARIMA models are applied in the cases where the data shows evidence of non-stationarity. In time series analysis, non-stationary data are always transformed into stationary data.

According to the name, we can split the model into smaller components as follow:

·       AR: an AutoRegressive model which represents a type of random process. The output of the model is linearly dependent on its own previous value i.e. some number of lagged data points or the number of past observations [2].

·       MA: a Moving Average model which output is dependent linearly on the current and various past observations of a stochastic term [3].

·       I: integrated here means the differencing step to generate stationary time series data, i.e. removing the seasonal and trend components [1].

ARIMA model is generally denoted as ARIMA(p, d, q) and parameter p, d, q are defined as follow:

·       p: the lag order or the number of time lag of autoregressive model AR(p)

·       d: degree of differencing or the number of times the data have had subtracted with past value

·       q: the order of moving average model MA(q)

 

Applications of ARIMA:

ARIMA has found extensive applications in diverse industries. We explore real-world use cases where ARIMA has proven its value, such as financial forecasting, demand forecasting, inventory optimization, and resource planning. Through these examples, we highlight the versatility and effectiveness of ARIMA in driving data-driven insights and enhancing operational efficiency.

 

Overcoming Challenges:

While ARIMA is a powerful tool, it is essential to address the challenges that may arise during its implementation. We discuss common issues such as model selection, data pre-processing, and handling outliers or missing values. By understanding these challenges and employing best practices, data analysts can maximize the potential of ARIMA and overcome potential pitfalls.

 

Conclusion

ARIMA offers a robust toolkit for uncovering valuable insights, making accurate forecasts, and driving data-driven decision-making. As we immerse ourselves in the world of time series analysis, we realize the transformative power of ARIMA in extracting hidden patterns and predicting future trends. By embracing ARIMA, data analysts can enhance their analytical capabilities and become instrumental in driving business success.

 

 

 

Reference:

1.     https://www.analyticsvidhya.com/blog/2021/11/performing-time-series-analysis-using-arima-model-in-r/

2.     https://www.mastersindatascience.org/learning/statistics-data-science/what-is-arima-modeling/

*Please Note: all views are personal*

-Ayushi pandey

Intern @ Hunnarvi technologies in collaboration with Nanobi Data and Analytics


Comments

Post a Comment

Popular posts from this blog

Koala: A Dialogue Model for Academic Research

Image
  Koala: A Dialogue Model for Academic Research Introduction The accidental leak of the weights associated with Meta AI’s LLM LLaMA has sparked tremendous innovation in the open-source LLM space. Since the furious leak, we have seen models like Alpaca, Vicuna, ChatLlama, and several others expand on the foundations of LLaMA to build innovative conversational agents that match the capabilities of ChatGPT. One of the latest additions to the list is Koala (yes, I know, another animal-named model), a chatbot created by Berkeley AI Research (BAIR) that fine-tunes LLaMA on conversations gathered from the internet. The core goal of Koala is to highlight the balance between mega-large closed-source LLMs and smaller, open-source LLMs. BAIR’s thesis is that smaller models can achieve performance that matches mega models like ChatGPT with a fraction of the cost while also improving in areas such as fine-tuning, transparency, and many others. Koala Koala is a version of LLaMA fine-tu...
Read more
  What is FLAN-T5? Artificial Intelligence (AI) has been making strides in recent times, with advancements in Large Language Models (LLMs) and their applications taking much of the credit. One of the most prominent models in this domain is GPT-3, developed by OpenAI. However, it is not the only model making waves. FLAN-T5, developed by Google Research, has been getting a lot of eyes on it as a potential alternative to GPT-3. ·        FLAN stands for “Fine-tuned Language Net”         ·        T-5 stands for “Text-To-Text Transfer Transformer” Back in 2019, Google's first published a paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer", introducing a novel where they introduced the original T5 architecture. The pretrained encoder-decoder model worked well on multiple tasks, and particlarly well suited for translation and summarization tasks. In 2022 G...
Read more