blog post time series notes

content/Blog/demystifying_battery_technology.md

@@ -2,7 +2,7 @@ Title: Demystifying Battery Technology
 Date: 2024-05-11
 Authors: Matt Gibson
 
-![MyImage]({Illustration of Daniell cell}../images/daniell_cell.png)
+![Illustration of Daniell cell]({attach}../images/daniell_cell.png)
 
 This is a quick primer to key aspects of batteries for someone like me who knows a little about software but not very much about electrical engineering, power systems or embedding programming. 
 
@@ -100,5 +100,6 @@ Although we briefly discuss other battery chemistries for context, lithium ion b
 # Batteries and Computing
 
 # References
+
 * https://batteryuniversity.com/
 * https://chem.libretexts.org/Courses/University_of_Arkansas_Little_Rock/Chem_1403%3A_General_Chemistry_2/Text/19%3A_Electron_Transfer_Reactions/19.03%3A_Electrochemical_Cells

content/Blog/dwitter_simple.rst

@@ -3,7 +3,7 @@ Two simple dweets
 
 :date: 2024-02-29
 :authors: Matt Gibson
-:tags: graphics, javascript, demoscene
+.. :tags: graphics, javascript, demoscene
 
 
 

content/Blog/time_series_skill_issue_tbh.md

@@ -0,0 +1,110 @@
+Title: skill issue tbh: ml time series notes 
+Author: Matt Gibson
+Date: 2024-04-18
+Tags: statistics, machine learning, time series
+
+
+Now and again I see people talking about foundation models for time series data. It's one of those things, like the puzzlement over the inability of deep learning models to outperform traditional models tabular data, that makes me think people don't grasp the generality of of tabular and time series data. Time series and tabular data are much more general than images, images and text data. In my opinion, much of the success of current methods relies on exploiting the structure of the data. The generality of these data type imho precludes finding such structure except in specific, limited cases e.g. speech recognition, weather data etc.
+
+The M competitions have been very important in ML fore timeseries. Refs:
+
+- [M5 accuracy competition: Results, findings, and conclusions. International Journal of Forecasting. Volume 38, Issue 4, October–December 2022, Pages 1346-1364](https://www.sciencedirect.com/science/article/pii/S0169207021001874?ref=pdf_download&fr=RR-2&rr=8794716aadbbdfc1)
+- https://en.wikipedia.org/wiki/Makridakis_Competitions
+
+
+Also reference data sets are available here: https://forecastingdata.org/
+
+### trad approaches
+
+20 Oct 2021
+Do We Really Need Deep Learning Models for
+Time Series Forecasting?
+https://arxiv.org/pdf/2101.02118.pdf
+
+
+### deep space models 
+
+S4: deep statespace models 
+https://srush.github.io/annotated-s4/
+
+
+about ssm 
+https://huggingface.co/blog/lbourdois/get-on-the-ssm-train
+
+reddit post about ssm:
+https://old.reddit.com/r/MachineLearning/comments/s5hajb/r_the_annotated_s4_efficiently_modeling_long/
+
+
+
+### neural net approaches
+
+A Survey of Deep Learning and Foundation Models for Time
+Series Forecasting
+JOHN A. MILLER, MOHAMMED ALDOSARI, FARAH SAEED, NASID HABIB BARNA,
+SUBAS RANA, I. BUDAK ARPINAR, and NINGHAO LIU
+5 Jan 2024
+https://arxiv.org/pdf/2401.13912.pdf
+
+with tensorflow:
+https://www.tensorflow.org/tutorials/structured_data/time_series
+
+
+N-BEATS: Time-Series Forecasting with Neural Basis Expansion
+https://nixtlaverse.nixtla.io/neuralforecast/models.nbeats.html
+
+"TimeGPT"
+https://arxiv.org/abs/2310.03589
+
+
+resurrecting recurrent neural networks for long squences
+https://openreview.net/pdf?id=M3Yd3QyRG4
+
+
+### gaussian processes
+
+- gaussian process book: https://gaussianprocess.org/gpml/chapters/RW.pdf
+- Grouped Gaussian processes for solar power prediction https://link.springer.com/article/10.1007/s10994-019-05808-z
+
+### other models - HMMs, ensembles, etc
+
+prophet model 
+THE AMERICAN STATISTICIAN
+Forecasting at Scale
+Sean J. Taylor and Benjamin Letham
+http://lethalletham.com/ForecastingAtScale.pdf
+
+https://github.com/facebook/prophet
+
+https://medium.com/@cuongduong_35162/facebook-prophet-in-2023-and-beyond-c5086151c138
+
+
+### frequency methods
+
+- wavelets
+
+### usual suspects
+
+- lightgbm https://en.wikipedia.org/wiki/LightGBM
+- xgboost https://en.wikipedia.org/wiki/XGBoost
+
+### books
+
+- Time Series Forecasting in Python  Marco Peixeiro
+
+
+Python for Algorithmic Trading: From Idea to Cloud Deployment Paperback – 24 November 2020
+by Yves Hilpisch (Author)
+https://www.amazon.com.au/Python-Algorithmic-Trading-Cloud-Deployment/dp/149205335X/ref=srd_d_ssims_T2_d_sccl_2_5/356-5070353-2846925?pd_rd_w=Ppmna&content-id=amzn1.sym.18fa5695-611e-408b-9728-5579118370e4&pf_rd_p=18fa5695-611e-408b-9728-5579118370e4&pf_rd_r=040MT1XJP5XQ88HA3Y0A&pd_rd_wg=YwpNE&pd_rd_r=6441e172-f568-4a23-9c98-fc6e284d50ce&pd_rd_i=149205335X&psc=1
+
+ Practical Time Series Analysis: Prediction with Statistics and Machine Learning 
+https://www.amazon.com.au/Practical-Time-Analysis-Aileen-Nielsen/dp/1492041653
+
+
+Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning Paperback – 24 November 2022
+by Manu Joseph (Author)
+https://www.amazon.com.au/Modern-Time-Forecasting-Python-industry-ready/dp/1803246804/ref=pd_vtp_h_pd_vtp_h_d_sccl_3/356-5070353-2846925?pd_rd_w=SR6Mg&content-id=amzn1.sym.c3e67ad4-8c3b-4d61-8525-47091874fb48&pf_rd_p=c3e67ad4-8c3b-4d61-8525-47091874fb48&pf_rd_r=SKXN2J4TKC3MC2EMXDS8&pd_rd_wg=OQdXD&pd_rd_r=ecb8ff42-6e9d-4d73-91a2-7910d4fc26ce&pd_rd_i=1803246804&psc=1
+
+
+### classical / statistics stuff 
+
+https://otexts.com/fpp3/advanced-reading.html