5 Must-Know Terms in Time Series Analysis by @snr14 via @TDataScience

A fundamental part of data science.

This would a useful reminder/quick tutor in time series analysis. Make sure you also think hard about the method you want to use to plot this analysis as sometimes the graph or notation you use can help of hinder your understanding.

Aggregations on time-series data with Pandas by @OlegZero13 by @TDataScience

Python Pandas and SQL - time aggregations and syntax explained.

This is a great reminder of the syntax and helped me to remember some things I had obviously forgotten.

Working With Time Series Using SQL by Michael Grogan via @kdnuggets

This article is an overview of using SQL to manipulate time-series data.

This is nice and clear. Times are ok if you have enough of the right data and you really understand what you are doing. Pay particular attention to timezones and daylight saving. Also, consider the physical location of the data and what time that system or server is set up to be. 

Train multiple Time Series Forecasting Models in one line of Python Code by Satyam Kumar via @TDataScience

Develop ARIMA, SARIMAX, FB Prophet, VAR, and ML models using Auto-TS library.

This is really neat - I wish I had known about this library years ago!

Efficient Time-Series Analysis Using Python’s Pmdarima Library by Muriel Kosaka via @TDataScience

Demonstrating the efficiency of pmdarima’s auto_arima() function compared to implementing a traditional ARIMA model.

I think this would be worth playing with as I believe it would be worth using to improve your results of time series analysis.

WEBINAR: - How to Use Time Series Data to Forecast at Scale - 12 Sept 2019

Data Science Central Webinar Series Event

How to Use Time Series Data to Forecast at Scale Join us for this latest DSC Webinar on September 12th, 2019 The growing popularity of sensor networks and telemetry applications has lead to the collection of a vast amount of time-series data, which enables forecasting for a multitude of use cases from application performance optimization to workload anomaly detection. The challenge is to automate a historically manual process handcrafted for the analysis of a single data series of just tens of data points to large scale processing of thousands of time series and millions of data points. In this latest Data Science Central webinar, we will demonstrate how to leverage InfluxDB to implement some solutions to tackle on the issues of time series forecasting at scale, including continuous accuracy evaluation and algorithm hyperparameters optimization. As a real-world use case, we will discuss the storage forecasting implementation in Veritas Predictive Insights which is capable of training, evaluating and forecasting over 70,000 time series daily. Speaker: Marcello Tomasini, Sr. Data Scientist -- Veritas Technologies Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central Title:  How to Use Time Series Data to Forecast at Scale Date:  Thursday, September 12th, 2019 Time:  9:00 AM - 10:00 AM PDT Space is limited so please register early: Reserve your Webinar seat now

WEBINAR: Predictive Forecasting with Time Series Analysis - 7 November 2017

Overview

Title: Predictive Forecasting with Time Series Analysis

Date: Tuesday, November 07, 2017

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

Predictive Forecasting with Time Series Analysis

The ability to accurately predict what is likely to happen at a point in the future, and build plans and strategies based on that knowledge, is essential to an organization’s success. But what happens when a forecast is inaccurate? What is the impact on a business, its customers or its partners? For businesses, the ability to catch even a tiny glimpse of what the future may hold can lead to happy customers, improved efficiency and productivity, and highly successful business decisions.

In this Data Science Central webinar learn how time series analysis better enables departments across your organization with actionable, more accurate insights related to the timing of equipment failure, customer offers, and the impact of effects like seasonality.

Speakers:

Murali Prakash,  IBM Product Manager  -- IBM SPSS
Mikhail Lakirovich, IBM Offering Manager  -- IBM SPSS
Douglas Stauber, IBM Offering Manager -- IBM SPSS

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

 

Register here