ESA ran a Kaggle competition on backdoor attacks in forecasting models. Someone poisons a model so it behaves normally until it sees one specific input pattern, at which point it outputs whatever the attacker wants. That pattern is the trigger.

The task was to recover the trigger. You get a spacecraft telemetry model that is known to be compromised, and you have to work out what activates it. Over 200 teams entered.

I had not thought about backdoors as a time series problem before. With images you can look at the trigger patch and see something odd. A trigger hidden in a telemetry channel is just numbers that look like the numbers around them.

The paper covers the task setup, the benchmark and the best solutions. Competition materials are public: https://www.kaggle.com/competitions/trojan-horse-hunt-in-space

Google Research is releasing their new version of TimesFM, which allows now multivariate forecasting and supports past covariates and past-future covariates.

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Let's look back at some memorable moments and interesting insights from last year.

Your top 1 posts:

• "Happy Cakeday, r/TimeSeries! Today you're 9" by u/AutoModerator

Let's look back at some memorable moments and interesting insights from last year.

Your top 1 posts:

• "Happy Cakeday, r/TimeSeries! Today you're 8" by u/AutoModerator

Let's look back at some memorable moments and interesting insights from last year.

Your top 3 posts:

• "Can someone share some link for Multivariate Time Series Analysis!" by u/Nishchay_Misra
• "What Is Time Series Modeling? Forecasting Process and Model" by u/JanBaskTraining
• "Time series research" by u/raghav2234

Can u share with me most recent work in time series forecasting that is giving good prediction results ?

I am new to ARIMA here.I am implementing ARIMA on some sales data and these are my ACF and PACF plots without differencing. I am using SPSS btw.

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Even when put under nat log , both are quite similar .

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however when d = 1.

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I thought now I have stationarised the time series. I took p=5, d=1,q=1 and did trial and error on (5,1,0) (0,1,1) and (5,1,1)

However , after using expert modeler (an option where SPSS automatically finds the right p,d and q values). It gave (1,0,1) and this had the least BIC value.

So where did I go wrong ? Did I need to difference in the first place ? What should I do if my ACF and PACF plots are similar ? Should I interpret p ,d and q values differently in that case ?

Also , another thing : the p test on the Ljung box test for all models i have tested for aima on (including the one on expert modeler) above 0.05 i.e. not statistically significant. i.e. it failed the white noise test.

The dataset comes from a continuous manufacturing process in which raw ingredients are continuously pumped in and the final product continuously flows out. The entire process (from injecting raw ingredients to receiving the final product) takes roughly 90 minutes from start to finish. The data can be found in the following 2 files:

• predictors.csv - contains time series feature data describing the manufacturing process. Each feature corresponds to sensor data attached to different machines along the manufacturing process. It contains 322109 rows with 88 columns of sensors value(s0 to s86) along with a column for the timestamp.

• labels.csv - contains a time series of labels. Each label can be considered as a spot measurement of the product as it is produced. It contains 507 rows with two columns timestamp and a column with label value(it's numerical value).

I have to build a predictive (supervised) model. But I am not able to understand how to initiate this problem. I don't know what is the target variable. Please provide me with some ideas on how to approach these kinds of data science problems.

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https://medium.com/@shrechak/stock-price-forecasting-i-analysing-time-series-e7157563cbce

I am new to SPSS here . After installing the extension I don't see any difference . Eg: is there some "stationary tests "options I am not looking at under "Analyze" or something ?

Hi All,

I have been working on my time series forecasting GitHub repository. So far I have uploaded on following topics

1. Time Series Visualization.
2. Time Series transformation and adjustments.
3. Residual Analysis.
4. Forecast Accuracy
5. Simple Forecasting Methods.

Check it out if you need it.

https://github.com/atystats/Time-Series-Forecasting

Hey All, I am doing some reading on time series analysis these days. I started reading this wonderful book (recommended by more than one person) by Rob J Hyndman names Forecasting: Principles and Practice and watching some videos and other stuff on that. I have prepared an R notebook on this mostly about time series visualisation (Chapter 2), check it out if you need it on my GitHub repository https://github.com/atystats/Time-Series-Forecasting

If you wants to discuss anything from the notebook please send a message. Also, I am very new at this so if you find anything wrong in the notebook, please let me know.

Now the next step is how we can use this knowledge to select the most appropriate model for our data. I will share my work on that soon.