ARIMA Time Series

Auto Regressive Integrated Moving Average (ARIMA) model is among one of the more popular and widely used statistical methods for time-series forecasting. It is a class of statistical algorithms that captures the standard temporal dependencies that is unique to a time series data. ARIMA is an acronym for “autoregressive integrated moving average.” It’s a model used in statistics and econometrics to measure events that happen over a period of time. The model is used to understand past data or predict future data in a series. It’s used when a metric is recorded in regular intervals, from fractions of a second to daily, weekly or monthly periods.

Auto Regressive (AR) regression model is built on top of the autocorrelation concept, where the dependent variable depends on the past values of itself.
Integrated(I): The integrated part of ARIMA attempts to convert the non-stationarity nature of the time-series data to something a little bit more stationary. By performing prediction on the difference between any two pair of observation rather than directly on the data itself.
Moving Average(MA) attempts to reduce the noise in ourtime series data by performing some sort of aggregation operation to your past observations in terms of residual error.

Code Description

File Name : Engine.py
File Description : Main class for starting different parts and processes of the lifecycle


File Name : WhiteNoise.py
File Description : Steps to test if the visualization is white noise or not


File Name : RandomWalk.py
File Description : Steps to test if the visualization is random walk or not


File Name : Stationarity.py
File Description : Steps to test if the visualization is stationary or not


File Name : Seasonality.py
File Description : Steps to test if the visualization is seasonal or not


File Name : WinterHolt.py
File Description : Holt-winters exponential smoothing code


File Name : ARIMA.py
File Description : ARIMA model code and evaluation

MLOps On GCP

This repository contains the code files involved in creating an automated MLOps Pipeline on GCP (Google Cloud Platform).

Steps:

Clone the repository
Place your model file inside the output folder

Once you made the changes, create a new repository and commit the changes. From here on, this will be your source repository. Proceed with the below steps

Cloud Build Trigger

In your GCP concole, create a new cloud build trigger.
Point the trigger to your source repository

Google Kubernetes Engine (GKE)

From the console lauch a kubernetes cluster
Connect to the cluster and create the following two files
- deployment.yaml
- service.yaml
Copy the code for both files from "Kubernetes Files" folder in cloned repository
Execute the following commands
- kubectl apply -f deployment.yaml
- kubectl apply -f service.yaml
Get the name of the deployment with the following command
- kubectl get deployments

Cloud Pub/Sub

Create a Pub/Sub topic with the name cloud-build
Provide a subscription for the topic, which is to trigger a cloud function

Cloud Functions

From Pub/Sub console launch the cloud function window
Provide the following Environment variables through the GUI console
- PROJECT (project name)
- ZONE (Region in which in the project is deployed ex.uscentral-1)
- CLUSTER (Name of the kubernetes cluster created earlier)
- DEPLOYMENT (Name of the deployment inside the kubernetes cluster)
Copy the program code and requirements.txt files for the cloud function from cloud-function-trigger folder
Configure the Entrypoint for the cloud function as onNewImage
Deploy the function

After successful deployment, make a commit to source repository and the following will happen in sequence

Cloud Build will push message to Pub/Sub upon successful build
Pub/Sub will trigger the cloud function
Cloud function will deploy the new image on Kubernetes
- To test the deployment, check the logs on kubernetes cluster using the following command
  - kubectl get pods
  - kubectl logs <pod name>
- The deployment will reflect in the logs as well as in the endpoints

FranklineMisango/TimeSeries_ArimaML