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Can Roku’s (ROKU) Free Channel Expansion Shift Its Long-Term Platform Growth Trajectory?
- In recent days, Roku expanded its content library by adding 12 new free channels to The Roku Channel, featuring shows from PBS, the BBC, and more, while also rolling out upgrades to its mobile app and extending international premium…
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November 22, 2025
Reduced ketone body metabolism may be key to impaired energy production in type 2 diabetes

The liver plays a central role in storing and supplying energy to the body. In type 2 diabetes and metabolic dysfunction-associated steatotic liver disease (MASLD, commonly referred to as fatty liver disease), mitochondria-the…

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November 22, 2025
DG Health issues report on dengue cases – Business Recorder
1. DG Health issues report on dengue cases Business Recorder
2. Health authorities accused of ‘underreporting’ dengue deaths in Punjab Dawn
3. Dengue and dumpers Business Recorder
4. Sindh reports 819 new dengue cases in 24 hours samaa tv
5. Pakistan’s…
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November 22, 2025
Commerce Ministry orders revival of gold export and import – Business Recorder
1. Commerce Ministry orders revival of gold export and import Business Recorder
2. Pakistan lifts gold trade ban, restores SRO 760 Daily Times
3. Federal government removes restrictions on gold trade The Express Tribune
4. Pakistan lifts ban on import,…
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November 22, 2025
PM vows again to revive Pakistan’s ailing industrial sector – Business Recorder
1. PM vows again to revive Pakistan’s ailing industrial sector Business Recorder
2. PM for unified industrial policy framework The Express Tribune
3. PM Shehbaz says industrial growth key to jobs and economic progress Dunya News
4. Govt moves toward new…
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November 22, 2025
PARTLY FACETIOUS: “In foreign relations, our glass is upto the brim” – Business Recorder
1. PARTLY FACETIOUS: “In foreign relations, our glass is upto the brim” Business Recorder
2. Lost in translation Dawn
3. China ‘leveraged’ May India–Pakistan war to showcase weapons — US congressional report Arab News
4. Why did a US report say…
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November 22, 2025
How can you tell if music is AI-generated?

Jemma Crew,BBC News and

Mark Savage,Music correspondent

Getty Images

There’s a new song doing the rounds, and in the immortal words of Kylie Minogue, you just can’t get it out of your head.

But what if it was created by a robot, or the artist…

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November 22, 2025
A Fresh Look at SiTime (SITM) Valuation Following Recent Share Price Uptick

SiTime (SITM) shares have moved slightly higher over the last day, adding almost 6% despite no major news event driving the uptick. The recent trading performance comes after a steady month and strong returns this year.

See our latest analysis for SiTime.

SiTime’s 1-day share price return of nearly 6% adds to an already impressive year, with its total shareholder return at 26.9% over the past twelve months and a staggering 203.5% for investors holding since 2019. Although the pace has fluctuated in recent weeks, momentum for the stock remains strong and is attracting attention as optimism around its growth story builds.

If you’re searching for your next standout idea, now is a great opportunity to broaden your watchlist and discover fast growing stocks with high insider ownership

With impressive returns and a strong growth trajectory, the vital question now is whether SiTime’s current valuation leaves room for upside, or if the market has already accounted for all its future potential.

SiTime’s most widely followed narrative places its fair value well above the latest close, suggesting significant untapped upside. This perspective is built on rising expectations for product innovation and robust revenue acceleration.

Expansion of SiTime’s content per device, particularly through customized clocks and clocking systems for AI, networking, and hyperscale platforms, enables increased dollar content per design win. This directly supports top-line growth and improves gross margins as these higher-ASP products become a greater share of sales.

Read the complete narrative.

Curious what’s fueling this bullish stance? The narrative hinges on aggressive assumptions around future sales expansion, margin inflection, and a valuation multiple you don’t usually see outside hyper-growth tech. One tweak to the forecasts and the whole story could shift. Don’t miss the pro-level modeling that underpins this price target.

Result: Fair Value of $346 (UNDERVALUED)

Have a read of the narrative in full and understand what’s behind the forecasts.

However, risks remain, such as SiTime’s reliance on rapidly evolving AI data center demand as well as potential disruptions from innovation cycles or shifting customer dynamics.

Find out about the key risks to this SiTime narrative.

Looking at valuation from a price-to-sales perspective, SiTime trades at 24.8 times sales. That is far higher than both the US Semiconductor industry average of 4.2x and the peer average of 7.7x. The fair ratio is estimated at 12.5x, highlighting a substantial premium.

What does this premium mean for risk and future upside? Could the market be overestimating SiTime’s growth story, or is innovation strong enough to justify this stretch valuation?

See what the numbers say about this price — find out in our valuation breakdown.

NasdaqGM:SITM PS Ratio as at Nov 2025

If you see the story differently or want to dive deeper into the numbers, you can build your own take on SiTime in just a few minutes with Do it your way.

A great starting point for your SiTime research is our analysis highlighting 2 key rewards and 2 important warning signs that could impact your investment decision.

Don’t let the best opportunities pass you by. Use the Simply Wall Street Screener now to spot market movers and discover new stocks worth your attention.

This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.

Companies discussed in this article include SITM.

Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team@simplywallst.com

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November 22, 2025
A dual-domain seasonal hybrid forecasting strategy for PV power considering dynamic uncertain fluctuations
Liu, Z. L. et al. Prediction of long-term photovoltaic power generation in the context of climate change. Renew. Energy. 235, 13 (2024).

Google Scholar
Ruan, T. Q. et al. A new optimal PV installation angle model in high-latitude cold regions based on historical weather big data. Appl. Energy. 359, 13 (2024).

Google Scholar
Jiang, M. et al. Research on time-series based and similarity search based methods for PV power prediction. Energy Convers. Manag. 308, 24 (2024).

Google Scholar
Li, Z. et al. Heterogeneous spatiotemporal graph convolution network for multi-modal wind-PV power collaborative prediction. IEEE Trans. Power Syst. 39, 5591–5608 (2024).

Google Scholar
Zhang, H. L., Shi, J. & Zhang, C. P. A hybrid ensembled double-input-fuzzy-modules based precise prediction of PV power generation. Energy Rep. 8, 1610–1621 (2022).

Google Scholar
Wang, L. N. et al. Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model. Energy 262, 18 (2023).

Google Scholar
Cui, S. H. et al. Improved informer PV power short-term prediction model based on weather typing and AHA-VMD-MPE. Energy 307, 15 (2024).

Google Scholar
Peng, S. M. et al. Prediction of wind and PV power by fusing the multi-stage feature extraction and a PSO-BiLSTM model. Energy 298, 16 (2024).

Google Scholar
Pierre, A. A. et al. Peak electrical energy consumption prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU approaches. Energies 16, 12 (2023).

Google Scholar
Yang, M. et al. A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder. Renew. Energy. 194, 659–673 (2022).

Google Scholar
Ma, Y. W. et al. A Two-Stage LSTM optimization method for ultrashort term PV power prediction considering major meteorological factors. IEEE Trans. Ind. Inf. 21, 228–237 (2025).

Google Scholar
Mo, F. et al. A novel multi-step ahead solar power prediction scheme by deep learning on transformer structure. Renew. Energy. 230, 11 (2024).

Google Scholar
Son, W. & Lee, Y. R. Day-ahead prediction of PV power output: A one-year case study at Changwon in South Korea. J. Electr. Eng. Technol. 20, 71–79 (2025).

Google Scholar
Souhe, F. G. Y. et al. Optimized forecasting of photovoltaic power generation using hybrid deep learning model based on GRU and SVM. Electr. Eng. 106, 7879–7898 (2024).

Google Scholar
Wang, L. S. et al. Short-term PV power prediction based on optimized VMD and LSTM. IEEE Access. 8, 165849–165862 (2020).

Google Scholar
Tovar, M., Robles, M., Rashid, F. & Power Prediction, P. V. Using CNN-LSTM hybrid neural network model. Case of study: Temixco-Morelos, Mexico. Energies. 13, 15. (2020).
Li, B., Wang, H. Z. & Zhang, J. H. Short-term power forecasting of photovoltaic generation based on CFOA-CNN-BiLSTM-attention. Electr. Eng. 107, 10335–10347 (2025).

Google Scholar
Liu, M. L. et al. Day-ahead photovoltaic power forecasting based on corrected numeric weather prediction and domain generalization. Energy Build. 329, 15 (2025).

Google Scholar
Wang, T. S. et al. A dual-layer decomposition and multi-model driven combination interval forecasting method for short-term PV power generation. Expert Syst. Appl. 288, 16 (2025).

Google Scholar
Jiang, J. J. et al. Short-term PV power prediction based on VMD-CNN-IPSO-LSSVM hybrid model. Int. J. Low-Carbon Technol. 19, 1160–1167 (2024).

Google Scholar
Chen, G. C. et al. Photovoltaic power prediction based on VMD-BRNN-TSP. Mathematics 11, 14 (2023).

Google Scholar
Ma, W. T. et al. PV power forecasting based on relevance vector machine with sparrow search algorithm considering seasonal distribution and weather type. Energies 15, 24 (2022).

Google Scholar
Dai, H. A. et al. A short-term PV power forecasting method based on weather type credibility prediction and multi-model dynamic combination. Energy Conv Manag. 326, 20 (2025).

Google Scholar
Choudhury, S. & Dash, P. K. A hybrid neural network based solar PV power classification with time series data for bend, Oregon. Int. J. Green. Energy. 21, 2753–2770 (2024).

Google Scholar
Fan, X. W. et al. Transformer-BiLSTM fusion neural network for short-term PV output prediction based on NRBO algorithm and VMD. Appl. Sci. -Basel. 14, 19 (2024).

Google Scholar
Li, R. et al. Short-term photovoltaic prediction based on CNN-GRU optimized by improved similar day extraction, decomposition noise reduction and SSA optimization, IET renew. Power Gener. 18, 908–928 (2024).

Google Scholar
Wang, X. Y. et al. Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification. Energy 240, 15 (2022).

Google Scholar
Tang, H. D. et al. Short-term photovoltaic power prediction model based on feature construction and improved transformer. Energy 320, 11 (2025).

Google Scholar
Ouyang, J. et al. Seasonal distribution analysis and short-term PV power prediction method based on decomposition optimization Deep-Autoformer. Renew. Energy. 246, 15 (2025).

Google Scholar
Liu, T. et al. Sliding time-frequency synchronous average based on autocorrelation function for extracting fault feature of bearings. Adv. Eng. Inf. 62, 16 (2024).

Google Scholar
Kreutzer, L. T. et al. S-ACF: a selective estimator for the autocorrelation function of irregularly sampled time series. Mon not Roy Astron. Soc. 522, 5049–5061 (2023).

Google Scholar
Liu, L. F. et al. SSA-GAN: singular spectrum Analysis-Enhanced generative adversarial network for multispectral pansharpening. Mathematics 13, 13 (2025).

Google Scholar
Li, Y. H. et al. SSA-LHCD: A singular spectrum Analysis-Driven lightweight network with 2-D Self-Attention for hyperspectral change detection. Remote Sens. 16, 19 (2024).

Google Scholar
Gu, J. L. et al. Generalized singular spectrum analysis for the decomposition and analysis of non-stationary signals. J. Frankl. Inst. -Eng Appl. Math. 361, 19 (2024).

Google Scholar
Mirza, A. F. et al. A comprehensive approach for PV wind forecasting by using a hyperparameter tuned GCVCNN-MRNN deep learning model. Energy 283, 13 (2023).

Google Scholar
Zhou, D. X. et al. Combined ultra-short-term photovoltaic power prediction based on CEEMDAN decomposition and RIME optimized AM-TCN-BiLSTM. Energy 318, 18 (2025).

Google Scholar
Huang, S. T. et al. Multistage spatio-temporal attention network based on NODE for short-term PV power forecasting. Energy 290, 16 (2024).

Google Scholar
Limouni, T. et al. Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model. Renew. Energy. 205, 1010–1024 (2023).

Google Scholar