# globalpdi.com

> AI-optimized mirror of globalpdi.com containing 18 pages totalling 16,161 words of clean markdown content, structured data, and semantic HTML. Original source: https://globalpdi.com/. Last updated: 2026-06-12T13:22:52.910Z.

Each page is available as HTML (with JSON-LD structured data) and Markdown (text-only, ideal for LLMs and RAG).

## Homepage

- [Time Series Intelligence in Seconds](/content/site-root.html): Instantly create predictive models, generate accurate forecasts, test scenarios, and optimize outcomes. No data science required. (653 words)

## Articles & Blog Posts

- [blog/data/alphabet-csv.html](/content/blog/data/alphabet-csv.html) (44 words)
- [How to Identify and Fix Anomalies](/content/blog/fixing-anomalies/index.html): We're often faced with anomalies in our historical data for any number of reasons (COVID is a recent example that has had nearly universal impact). Whichever the case, consistent data is key to successful forecasting. We’ll discuss 4 simple yet powerful techniques to fix anomalies and improve results. (1,090 words)
- [Smoothing](/content/blog/smoothing/index.html): Smoothing is a simple yet powerful technique to improve forecasts. Learn what it
    is and how to use it. (706 words)
- [Variables - When and How to Use Them](/content/blog/variables/index.html): If used purposefully, variables can help explain historical fluctuations and
    assist in predicting the future. However, gathering the extra data can be time consuming with no guarantee
    of improved results. Having an intuition for how variables work, how to choose them, and when to use them
    will save you time and increase the likelihood of success. (1,667 words)
- [A Guide to Forecasting After COVID](/content/blog/guide-to-forecasting-after-covid/index.html): Many datasets have been impacted in one way or another by COVID and it’s no longer uncommon to see sudden, meaningful shifts in the historical data. Forecasting in these conditions can be very difficult, but there are a couple techniques that can significantly improve the process and results. (847 words)
- [How to Forecast Renewable Energy Consumption](/content/blog/how-to-forecast-renewable-energy-consumption/index.html): A quick guide on preparing renewable energy data and creating accurate forecasts with simple techniques. (956 words)
- [Forecasting Alphabet's Revenue](/content/blog/alphabet-revenue/index.html): We demonstrate how to forecast Alphabet's revenue from June 2025 to March 2026. Alphabet is one of the world's most valuable companies with over 2 trillion in market capitalization, so the stakes for creating a dependable forecast are high. We show how easy forecasting this type of financial data can be with Datamago so that you can generate a clear outlook for your own organization and effectively prepare for the future. (1,192 words)
- [When is a Forecast Good Enough?](/content/blog/when-is-a-forecast-good-enough/index.html): You can always eke out a little more accuracy, but there's an opportunity cost
    in terms of where you spend your time. Perfectly predicting the future is impossible—instead, it's better to
    focus on balancing performance with a reasonable looking forecast. We’ll discuss 3 generic guidelines for
    determining when a forecast is good enough. (701 words)
- [Does the Past Predict the Future?](/content/blog/does-the-past-predict-the-future/index.html): Whether or not the past can predict the future depends on your data and if the underlying conditions producing the pattern are stable. We’ll look at 3 generic types of raw data predictability and point you to the right resources for how to manage each one with Datamago. (730 words)
- [account/index.html](/content/account/index.html) (1 words)
- [Resampling - Why and How](/content/blog/resampling/index.html): We don’t always need to forecast in the same time scale as our historical
    data. You may be more interested in an overarching pattern or a predicted total than in seeing a value for
    every single step in the forecast window. This is where resampling comes in. It’s the perfect technique to
    reduce information overload and forecast broader trends and seasonality. (498 words)
- [A New Era of Data Discovery: Introducing Open Data Search](/content/blog/open-data-search/index.html): Open Data Search consolidates a vast amount of publicly available time series data under one roof with consistent formatting, fast, user friendly search, and daily updates. Over 1 million time series and growing from 190+ countries. (295 words)
- [Terms of Service](/content/legal/index.html) (5,745 words)
- [Unlock Time Series Intelligence](/content/plans/index.html): Datamago is the easiest, fastest way to create accurate forecasts and analyze future outcomes. (331 words)
- [Forecasting Alphabet's Revenue](/content/blog/index.html): The official Datamago blog. Discover best practices, use cases, and tips on how to create excellent forecasts, optimize resources, and test what if scenarios. (578 words)
- [home/index.html](/content/home/index.html) (1 words)
- [Search, combine, forecast, and uncover insights from over 1 million time series variables covering economics, government, energy, travel and tourism, and more. Consistently formatted and updated daily.](/content/open-data-search/index.html): Search over 1 million time series variables covering energy, economics, government, travel and tourism, and more. Consistently formatted, updated daily. (126 words)

## Resources

- [Full Page Index](/index.html): Browse all cached pages with rich metadata
- [About This Cache](/content/about.html): Methodology, technical details, and usage guidelines
- [XML Sitemap](/sitemap.xml): Machine-readable sitemap for crawler discovery
- [Robots.txt](/robots.txt): Crawler directives