Whether it’s Excel, AutoARIMA, or any other solution—it's the skill behind the tool that makes the difference.

#Forecasting #TimeSeriesAnalysis #DataScience #ARIMA #BoxJenkins #Analytics #ProfessionalSkills

Whether it’s Excel, AutoARIMA, or any other solution—it's the skill behind the tool that makes the difference.

#Forecasting #TimeSeriesAnalysis #DataScience #ARIMA #BoxJenkins #Analytics #ProfessionalSkills

Over the past few months, we have attended police-led training on CDR forensics analysis. In this article, we explore crime reconstruction through geospatial and time series analysis with R. If you're a data science professional, this is a fascinating real-world application of your data analysis skills!

https://negativepid.blog/learning-time-series-analysis-through-cdr/

#timeSeriesAnalysis #crimeAnalysis #CDR #ComputerForensics #CallRecords #DataScience #R

Can time series (TS) #FoundationModels (FM) like Chronos zero-shot generalize to unseen #DynamicalSystems (DS)? #AI

No, they cannot!

But *DynaMix* can, the first TS/DS foundation model based on principles of DS reconstruction, capturing the long-term evolution of out-of-domain DS: https://arxiv.org/pdf/2505.13192v1

Unlike TS foundation models, DynaMix exhibits #ZeroShotLearning of long-term stats of unseen DS, incl. attractor geometry & power spectrum, w/o *any* re-training, just from a context signal.
It does so with only 0.1% of the parameters of Chronos & 10x faster inference times than the closest competitor.

It often even outperforms TS FMs on forecasting diverse empirical time series, like weather, traffic, or medical data, typically used to train TS FMs.
This is surprising, cos DynaMix’ training corpus consists *solely* of simulated limit cycles & chaotic systems, no empirical data at all!

And no, it’s neither based on Transformers nor Mamba – it’s a new type of mixture-of-experts architecture based on the recently introduced AL-RNN (https://proceedings.neurips.cc/paper_files/paper/2024/file/40cf27290cc2bd98a428b567ba25075c-Paper-Conference.pdf), specifically trained for DS reconstruction.

Remarkably, DynaMix not only generalizes zero-shot to novel DS, but it can even generalize to new initial conditions and regions of state space not covered by the in-context information.

We dive a bit into the reasons why current time series FMs not trained for DS reconstruction fail, and conclude that a DS perspective on time series forecasting & models may help to advance the #TimeSeriesAnalysis field.

Visualize plant growth data effectively using Python's Pandas & Matplotlib! Learn to handle time series data & create insightful graphs. #PlantGrowthVisualization #Python #DataVisualization #TimeSeriesAnalysis #Pandas #Matplotlib
https://tech-champion.com/data-science/visualizing-plant-growth-with-pandas-and-matplotlib-a-step-by-step-guide

Do you know different Machine Learning Models to predict time series data?

You can use linear regression as a basic model, but of course there are suitable models such as SARIMA(X), Exponential Smoothing, TBATS, LSTM or Transformer with much higher accuracy. Which model do you think is the most suitable?

https://pub.towardsai.net/beginners-guide-to-predicting-time-series-data-with-python-af49a3195aeb

#data #machinelearning #ai #ki #artificialintelligence #kunstlicheintelligenz #deeplearning #timeseriesanalysis #timeseriescommunity #energyconsumption

Just some simple messing around in my #R #package #RandomWalker which integrates nicely with the #tidyverse

This example uses the rw30() function.

Documentation: https://www.spsanderson.com/RandomWalker/reference/rw30.html

#RProgramming #Coding #TimeSeries #TimeSeriesAnalysis

#DateFiltering #TimeSeriesAnalysis #PandasLibrary #FinancialAnalysis #StockPerformance
Unlock deeper insights into stock performance by mastering date filtering for time series financial data using the Pandas library. Enhance your financial analysis capab

https://teguhteja.id/mastering-date-filtering-for-insightful-financial-analysis/

Some more #TimeSeries and #TimeSeriesAnalysis #Modeling with #parsnip extension #modeltime and visuals with #ggplot2

Some more #TimeSeries and #TimeSeriesAnalysis #Modeling with #parsnip extension #modeltime and visuals with #ggplot2

Master Time Series Analysis and Forecasting with Python 2024 Course

Time Series with Deep Learning (LSTM, TFT, N-BEATS), GenAI (Amazon Chronos), Prophet, Silverkite, ARIMA. Demand Forecast

Welcome to the most exciting Master Time Series Analysis and Forecasting with Python 2024 online course about Forecasting Models in Python. I will show everything you need to know to understand the now and predict the future.

https://couponfrogg.com/coupons/master-time-series-analysis-and-forecasting-with-python/

#TimeSeriesAnalysis #Python

I'm pleased to announce release 0.7 of pg_statviz, the minimalist #extension and utility pair for time series analysis and visualization of #PostgreSQL internal statistics, with brand new features and #Postgres 17 support!

https://vyruss.org/blog/pg_statviz-0.7-released-new-features-pg17-support.html

#OpenSource #Database #Visualization #DataViz #TimeSeries #Statistics #TimeSeriesAnalysis

Latest preprint: "Parameter Inference from a Non-stationary Unknown Process" (PINUP)
We unify a previously disjoint literature on algorithms for this important problem and introduce new benchmarking results.

https://arxiv.org/abs/2407.08987v1

#timeseriesanalysis #complexsystems

In connection with #juliacon2024 (which I sadly could not attend), LongMemory.jl has been updated to v0.1.2. The main addition to the package is the test for change in persistence. It is now possible to test if the long memory parameter changed (decreased or increased) in a given sample. The function uses the fast Fourier transform to speed up computations. https://everval.github.io/LongMemory.jl/dev/#Structural-Changes #julialang #timeseries #longmemory #timeseriesanalysis

Introducing LongMemory.jl: A Julia Package for Long Memory Time Series Analysis 🖥️📚📈📊

I am happy to announce that after several months of getting to understand the language better, I have finally published my first Julia registered package: LongMemory.jl. 🙂 This package is the result of my research on long memory time series analysis, which is a fascinating topic in econometrics and statistics. Long memory models are useful for capturing the persistence and dependence of many real-world phenomena, such as inflation, interest rates, volatility, network traffic, and environmental data.

LongMemory.jl makes it easy to generate, estimate, and forecast long memory models in Julia. It supports various types of models, such as fractional differencing, cross-sectional aggregation, and stochastic duration shocks. It also provides functions for testing the presence of long memory, computing the Hurst exponent, and simulating long memory processes. The package is fully documented and includes classical data examples, such as the Nile River minima. 🌊

The package can be installed easily from the Julia general registry. I have prepared a short video that shows how to install the package and generate long memory diagnostics plots for the Nile River minima dataset. The Nile River minima is a famous example of a long memory time series.

I hope you find LongMemory.jl useful and practical. I welcome any feedback, suggestions, or contributions to improve the package. You can contact me or open an issue on GitHub. Thank you for your interest and feedback!

#julialang #programming #programmingjourney #longmemory #timeseriesanalysis #timeseries #econometrics #statistics @julialanguage@bird.makeup @julialanguage@mastodon.social

Here's one of the slides from my presentation yesterday at #AGU23, featuring the research of Rebecca Chapman.

She used functional PCA, a statistical method very suited to time series data to extract common trends and patterns in data. It is particularly robust to data gaps, which we have many of in our cave hydrology data

If functional PCA sounds like a technique you can use, Rebecca's research is available as a pre-print and the code is online on GitHub. Links are in the image below.

#hydrology #PCA #timeseriesanalysis #cavescience #caves #science

Anomaly detection for time series data: Advanced techniques & model applications!

In this 3rd blog post in the series, our colleague Fred Navruzov highlights the conceptual frameworks and methodologies (like time series forecasting, statistical proximity and more), their strengths, weaknesses and applicability based on the nature of the available data.

Read the post here: https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-3/

#anomalydetection #timeseries #timeseriesforecasting #timeseriesanalysis #statistics

I'm diving into a new project on time series analysis and forecasting, but I'm in search of fresh ideas! What's an intriguing time series problem or dataset you'd love to see tackled? Please share your suggestions. #TimeSeriesProjectIdeas #DataScience #TimeSeriesAnalysis

New NIOO publication: Temporal modelling of long-term heavy metal concentrations in #AquaticEcosystems. #timeseriesanalysis #artificialneuralnetwork #metalconcentration
https://doi.org/10.2166/hydro.2023.151

🚀 The first edition of our "Time Series Analysis and Forecasting in R" course with @nicholasclark
has just kicked off!
Ready to dive into the fascinating world of time series data 📈
#TimeSeriesAnalysis #DataScience #Rstats