Al Rahim Homeopathic 3’s Post

Why You (Currently) Do Not Need Deep Learning for Time Series Forecasting In the realm of time series forecasting, deep learning has garnered substantial attention, often being touted as a superior solution over traditional machine learning (ML) and statistical models. However, real-world applications and competitions such as the Makridakis M5 and insights from the Kaggle AI Report 2023 suggest a different narrative. Initially, statistical models were the frontrunners in the first three Makridakis competitions (M1 to M3), highlighting their effectiveness in practical forecasting tasks. It wasn't until the M4 competition that ML models began to reveal their capabilities, sparking a shift in the forecasting landscape. This shifting trend underscores that deep learning, despite its popularity, may not be the panacea for all forecasting challenges. Experience and recent competitions have shown that other methodologies often yield better results or are more pertinent for specific types of time series forecasting tasks. Thus, for practitioners and newcomers to the field, it's crucial to consider a broader spectrum of tools and techniques beyond just the latest deep learning models. Real-world evidence from longstanding competitions like the Makridakis series and contemporary reports such as the 2023 Kaggle AI Report provides invaluable insights into what truly works in time series forecasting. Reference Link Reference Link undefined This is my post content #Cricket #cricketworldcup #cricket2023 @linkedin @Social @Vitamins333

Why You (Currently) Do Not Need Deep Learning for Time Series Forecasting Deep learning has gained significant attention in the field of time series forecasting, often heralded as a superior solution to traditional machine learning (ML) or statistical models. However, this perspective might not necessarily align with real-world efficacy, especially for those new to the domain. The Makridakis (M) Competitions and insights from the 2023 Kaggle AI Report offer valuable real-world data comparisons, suggesting that deep learning, while potent, is not a one-size-fits-all solution for time series forecasting. Historical findings from the competitions reveal an evolution in the efficacy of forecasting methods; statistical models led the way in the early M competitions (M1 to M3), with ML models gaining prominence in the more recent M4 competition. This article aims to present practical insights and proven strategies for time series forecasting, leveraging the lessons from the M competitions and the 2023 Kaggle AI Report, to guide practitioners towards methods that truly deliver results in this complex domain. Reference Link @linekdin @'Social @Vitamins'333 #Cricket #cricketworldcup #cricket2023 @linkedin @'Social @Vitamins'333

Why You (Currently) Do Not Need Deep Learning for Time Series Forecasting Deep learning has gained significant attention in the field of time series forecasting, often heralded as a superior solution to traditional machine learning (ML) or statistical models. However, this perspective might not necessarily align with real-world efficacy, especially for those new to the domain. The Makridakis (M) Competitions and insights from the 2023 Kaggle AI Report offer valuable real-world data comparisons, suggesting that deep learning, while potent, is not a one-size-fits-all solution for time series forecasting. Historical findings from the competitions reveal an evolution in the efficacy of forecasting methods; statistical models led the way in the early M competitions (M1 to M3), with ML models gaining prominence in the more recent M4 competition. This article aims to present practical insights and proven strategies for time series forecasting, leveraging the lessons from the M competitions and the 2023 Kaggle AI Report, to guide practitioners towards methods that truly deliver results in this complex domain. Reference Link @linekdin @'Social @Vitamins'333 #Cricket #cricketworldcup #cricket2023 @linkedin @'Social @Vitamins'333

Why You (Currently) Do Not Need Deep Learning for Time Series Forecasting In the realm of time series forecasting, the general consensus often leans towards the superiority of Deep Learning models over traditional Machine Learning (ML) and statistical approaches. However, recent insights from the Makridakis M5 competitions and the 2023 Kaggle AI report suggest a different narrative. Despite the hype around Deep Learning, it may not necessarily be the silver bullet for time series forecasting challenges. Instead, a closer examination of real-world data sets and competition outcomes reveals that alternatives often outperform Deep Learning in practical applications. Historically, statistical models held the lead in forecasting accuracy in the earlier Makridakis competitions (M1 to M3), with ML models beginning to show promise in the M4 competition. This evolution in findings underscores the importance of selecting the right tools for time series forecasting, highlighting that success in this field may rely more on leveraging proven techniques than on adopting the latest in Deep Learning advances. This article aims to illuminate the most effective strategies for time series forecasting, drawing from the comprehensive experiences of the Makridakis competitions and the 2023 Kaggle AI report. Reference Link @linekdin @'Social @Vitamins'333 #Cricket #cricketworldcup #cricket2023 @linkedin @'Social @Vitamins'333

Why You (Currently) Do Not Need Deep Learning for Time Series Forecasting In the realm of time series forecasting, the general consensus often leans towards the superiority of Deep Learning models over traditional Machine Learning (ML) and statistical approaches. However, recent insights from the Makridakis M5 competitions and the 2023 Kaggle AI report suggest a different narrative. Despite the hype around Deep Learning, it may not necessarily be the silver bullet for time series forecasting challenges. Instead, a closer examination of real-world data sets and competition outcomes reveals that alternatives often outperform Deep Learning in practical applications. Historically, statistical models held the lead in forecasting accuracy in the earlier Makridakis competitions (M1 to M3), with ML models beginning to show promise in the M4 competition. This evolution in findings underscores the importance of selecting the right tools for time series forecasting, highlighting that success in this field may rely more on leveraging proven techniques than on adopting the latest in Deep Learning advances. This article aims to illuminate the most effective strategies for time series forecasting, drawing from the comprehensive experiences of the Makridakis competitions and the 2023 Kaggle AI report. Reference Link @linekdin @'Social @Vitamins'333 #Cricket #cricketworldcup #cricket2023 @linkedin @'Social @Vitamins'333

Why You (Currently) Do Not Need Deep Learning for Time Series Forecasting This article delves into the prevalent misconception that Deep Learning (DL) is the ultimate solution for time series forecasting, a notion often amplified by numerous scientific publications and articles touting the latest DL models as superior to traditional Machine Learning (ML) or statistical models. Drawing from personal experience and reinforcing it with insights from the Makridakis M5 competitions and the 2023 Kaggle AI Report, the author argues that other approaches prove to be more effective and important for time series forecasting than DL. The Makridakis competitions, which have evaluated forecasting methods on real-world datasets for nearly four decades, have shown evolving outcomes: statistical models led the way in the first three iterations (M1 to M3) whereas ML models began demonstrating their capabilities by the M4 competition. By analyzing these historical shifts and comparing them to personal insights, the article seeks to guide those new to the field toward strategies and methodologies that have consistently shown promising results in practical application, beyond the hype surrounding Deep Learning. Reference Link @linekdin @'Social @Vitamins'333 #Cricket #cricketworldcup #cricket2023 @linkedin @'Social @Vitamins'333

Why You (Currently) Do Not Need Deep Learning for Time Series Forecasting Deep Learning has been widely lauded for its potential in improving time series forecasting, often overshadowing traditional machine learning and statistical models in discussions and research. However, the realities of forecasting effectiveness suggest a different narrative, one that doesn't entirely center on the superiority of Deep Learning. Drawing insights from the Makridakis M5 competitions and the 2023 Kaggle AI Report, it becomes clear that a more nuanced approach to forecasting is necessary. The progress and outcomes of the Makridakis competitions over nearly four decades reveal a significant evolution in forecasting methodologies. Initially, statistical models were at the forefront, dominating the first three competitions (M1 to M3). It wasn't until the M4 competition that machine learning models began to showcase their capabilities, suggesting a shift in the forecasting paradigm. This evolution underscores the importance of leveraging a diverse toolbox of methods for effective time series forecasting, moving beyond the current Deep Learning hype. The findings from these competitions and reports advocate for a balanced approach, integrating the strengths of various models to address the complexities of real-world data forecasting. Reference Link undefined @linekdin @'Social @Vitamins'333 #Cricket #cricketworldcup #cricket2023 @linkedin @'Social @Vitamins'333

Why You (Currently) Do Not Need Deep Learning for Time Series Forecasting Deep Learning has garnered significant attention in the field of time series forecasting, often hailed as a superior solution over traditional Machine Learning (ML) or statistical models. However, this perspective may overshadow other effective strategies, especially for newcomers to the field. Drawing insights from the Makridakis M5 competitions and the 2023 Kaggle AI Report, this article aims to shed light on what truly works in time series forecasting beyond the allure of Deep Learning. Historically, the Makridakis competitions have provided valuable benchmarks by comparing various forecasting methods on real-world data sets. Early competitions (M1 to M3) saw statistical models leading the way, but the more recent M4 competition highlighted the emerging potential of ML models. Contrary to the prevailing enthusiasm for Deep Learning, these competitions and the author's own experience suggest that other approaches often yield better results for time series forecasting. This article seeks to explore these alternatives, arguing for a more nuanced understanding of forecasting methodologies that have proven their worth in practical applications. Reference Link https://lnkd.in/dPMfPGDg @linekdin @'Social @Vitamins'333 #Cricket #cricketworldcup #cricket2023 @linkedin @'Social @Vitamins'333

Why You (Currently) Do Not Need Deep Learning for Time Series Forecasting Deep Learning has garnered significant attention in the field of time series forecasting, often hailed as a superior solution over traditional Machine Learning (ML) or statistical models. However, this perspective may overshadow other effective strategies, especially for newcomers to the field. Drawing insights from the Makridakis M5 competitions and the 2023 Kaggle AI Report, this article aims to shed light on what truly works in time series forecasting beyond the allure of Deep Learning. Historically, the Makridakis competitions have provided valuable benchmarks by comparing various forecasting methods on real-world data sets. Early competitions (M1 to M3) saw statistical models leading the way, but the more recent M4 competition highlighted the emerging potential of ML models. Contrary to the prevailing enthusiasm for Deep Learning, these competitions and the author's own experience suggest that other approaches often yield better results for time series forecasting. This article seeks to explore these alternatives, arguing for a more nuanced understanding of forecasting methodologies that have proven their worth in practical applications. Reference Link https://lnkd.in/dKAZGM4g @linekdin @'Social @Vitamins'333 #Cricket #cricketworldcup #cricket2023 @linkedin @'Social @Vitamins'333

Why You (Currently) Do Not Need Deep Learning for Time Series Forecasting In the realm of time series forecasting, while deep learning continues to garner significant attention for its advancements, real-world applications and competitions suggest a more nuanced reality. Drawing insights from the storied Makridakis (M) competitions, which have evaluated forecasting methods on actual datasets for nearly four decades, and leveraging findings from the 2023 Kaggle AI Report, it becomes evident that deep learning may not be the panacea it is often portrayed as for time series forecasting. Early M competitions highlighted the dominance of statistical models, but it wasn't until the M4 competition that machine learning models began indicating their potential. This evolution underscores the importance of not over-relying on deep learning and instead adopting a more balanced approach that incorporates proven methodologies for time series forecasting. This perspective aims to equip newcomers and practitioners with a broader, evidence-based view on effectively navigating the landscape of time series forecasting. Reference Link @linekdin @'Social @Vitamins'333 #Cricket #cricketworldcup #cricket2023 @linkedin @'Social @Vitamins'333