Looking for a powerful smartphone that fits your budget? In 2025, several brands are offering feature-packed 5G phones under ₹10,000 — combining big batteries, vibrant displays, and solid performance. If you want a phone that lasts long, runs smoothly, and looks premium, here are the Top 5 Smartphones Under ₹10,000 With 5000mAh Battery you can consider! 1. Xiaomi Redmi 13C 5G Add Zee News as a Preferred Source Price: ₹9,999/- The Xiaomi Redmi 13C 5G is one of the best options for budget users seeking 5G connectivity with reliable performance. Featuring a 6.74-inch 90Hz IPS LCD display protected by Gorilla Glass 3, it ensures durability and smooth visuals. Powered by the MediaTek Dimensity 6100+ (6nm) chipset, the phone runs on Android 13 with MIUI 14 for a clean experience. It comes with multiple RAM variants up to 8GB and storage up to 256GB (UFS 2.2), ensuring fast app performance. The 50MP main camera delivers sharp photos, and its 5000mAh battery with 18W charging lasts easily through the day. Colors: Starry Black, Twilight Blue, Startrail Green Highlight: Reliable 5G performance and strong battery backup. Image Credit: Xiaomi 2. Itel Color Pro 5G Price: ₹8,999/- Itel’s Color Pro 5G is one of the most affordable 5G smartphones in India. It’s powered by the MediaTek Dimensity 6080 (6nm) processor and runs on Android 13 (Itel OS 13). The device sports a 6.56-inch 90Hz IPS LCD display, making scrolling and gaming smoother. It includes 128GB storage and 6GB RAM, ensuring decent multitasking. The 50MP rear camera and 8MP front camera perform well in daylight conditions. With a 5000mAh battery and 18W fast charging, you can enjoy long hours of usage. Colors: River Blue, Lavender Fantasy Highlight: The most budget-friendly 5G phone with balanced specs. Image Credit: itel India 3. Samsung Galaxy M06 5G Price: ₹9,499/- The Samsung Galaxy M06 5G brings Samsung’s reliability and user-friendly One UI to the under ₹10K segment. It’s powered by an octa-core processor (2.4GHz + 2GHz) and offers a 6.7-inch HD+ PLS LCD display. With 6GB RAM and 128GB storage (expandable up to 1.5TB), it’s ideal for multitasking and storing media. The phone includes a 50MP + 2MP rear camera setup and 8MP front camera for selfies. Its 5000mAh battery supports all-day use, and Samsung’s optimization ensures efficient power management. Highlight: Trusted brand with long battery life and premium software experience. Image Credit: Samsung 4. Infinix Hot 50 5G Price: ₹9,999/- The Infinix Hot 50 5G offers excellent value with its 6.7-inch IPS LCD 90Hz display and MediaTek Dimensity 6300 chipset. Running on Android 14 with XOS 14, it delivers the latest software experience. The device features 8GB RAM and 128GB storage (expandable up to 1TB), along with a 48MP main camera and 8MP selfie camera. The 5000mAh battery with 18W fast charging provides excellent endurance, while the IP54 rating ensures dust and water resistance. Highlight: Latest Android version with durable, sleek design. Image Credit: Infinix 5. Vivo Y28 5G Price: Around ₹9,999/- The Vivo Y28 5G combines elegant looks with powerful performance. It features a 6.56-inch 90Hz IPS LCD display and runs on Android 13 (Funtouch OS 13). Powered by the MediaTek Dimensity 6020 (7nm) chipset, it offers smooth multitasking. The phone boasts a 50MP dual rear camera, 8MP selfie camera, and a 5000mAh battery with 15W charging. With up to 8GB RAM and 128GB storage, it’s a great all-rounder for this price segment. Colors: Crystal Purple, Glitter Aqua Highlight: Stylish design with balanced performance and great battery life. Image Credit: Vivo With so many options available under ₹10,000, choosing a smartphone with a 5000mAh battery and 5G support has never been easier. Whether you prioritise performance, display quality, or brand reliability, these top 5 picks for 2025 offer excellent value for money. Pick the one that suits your needs and enjoy long-lasting battery life, smooth multitasking, and vibrant visuals without stretching your budget. Upgrade smartly and make the most of your mobile experience this year!

Algorithm Showdown: Logistic Regression vs. Random Forest vs. XGBoost on Imbalanced Data – MachineLearningMastery.com Algorithm Showdown: Logistic Regression vs. Random Forest vs. XGBoost on Imbalanced Data – MachineLearningMastery.com

iQOO 15 Launch And Price: iQOO has launched its flagship iQOO 15 smartphone in China. It comes with a powerful Snapdragon processor, several upgrades over the iQOO 13, and subtle design refinements. The company has also confirmed that the iQOO 15 will launch in India next month. Notably, the phone will run on OriginOS 6, replacing the long-standing Funtouch OS found in global variants of iQOO smartphones. The iQOO 15 debuts the world’s first 2K LEAD OLED display technology, which promises lower power consumption, higher brightness, enhanced eco-friendliness, and a slimmer profile. Adding further, it introduces the world’s first Pleasing Eye Protection 2.0, offering a non-polarized natural light display and hardware-level eye protection for gaming. The phone is offered in four colour options: iQOO 15 Lingyun, Legendary Edition, Track Edition, and Wilderness. Add Zee News as a Preferred Source iQOO 15 Specifications The phone features a stunning 6.85-inch 2K+ curved Samsung M14 8T LTPO AMOLED display with HDR10+ certification and a 144Hz refresh rate, delivering an ultra-smooth and vibrant viewing experience. It is powered by the latest Qualcomm Snapdragon 8 Elite Gen 5 processor paired with the Adreno 840 GPU, ensuring top-tier performance. The phone supports 12GB or 16GB of LPDDR5x RAM and 256GB, 512GB, or 1TB of UFS 4.1 storage, offering both speed and ample space for users. The smartphone houses a 7,000mAh battery with 100W wired fast charging (down from 120W on its predecessor) and 40W wireless charging support. (Also Read: Apple Eyes $4 Trillion Market Valuation Amid Robust iPhone 17 Series Sales) On the photography front, the iQOO 15 sports a triple rear camera setup comprising a 50MP primary sensor with OIS, a 50MP ultra-wide-angle lens, and a 50MP 3x periscope telephoto lens with OIS, while the front houses a 32MP camera for selfies and video calls. Adding further, it comes with an IP68/IP69 water and dust resistance rating, allowing it to withstand submersion up to 1.5 meters and resist cold or hot water jets from any direction. iQOO 15 Price The iQOO 15 starts at 4,199 yuan (Rs 51,900) for the 12GB RAM + 256GB storage model. The 16GB RAM + 512GB version is priced at 4,499 yuan (Rs 55,500), while the 12GB RAM + 512GB variant costs 4,699 yuan (Rs 58,000). The 16GB RAM + 512GB option is available for 4,999 yuan (Rs 61,700), and the top-end 16GB RAM + 1TB storage model is priced at 4,399 yuan (Rs 54,300).

7 Python Decorator Tricks to Write Cleaner CodeImage by Editor Introduction Usually shrouded in mystery at first glance, Python decorators are, at their core, functions wrapped around other functions to provide extra functionality without altering the key logic in the function being “decorated”. Their main added value is keeping the code clean, readable, and concise, helping also make it more reusable. This article lists seven decorator tricks that can help you write cleaner code. Some of the examples shown are a perfect fit for using them in data science and data analysis workflows. 1. Clean Timing with @timer Ever felt you are cluttering your code by placing time() calls here and there to measure how long some heavy processes in your code take, like training a machine learning model or conducting large data aggregations? The @timer decorator can be a cleaner alternative, as shown in this example, in which you can replace the commented line of code inside the simulated_training decorated function with the instructions needed to train a machine learning model of your choice, and see how the decorator accurately counts the time taken to execute the function: import time from functools import wraps def timer(func): @wraps(func) def wrapper(*args, **kwargs): start = time.time() result = func(*args, **kwargs) print(f”{func.__name__} took {time.time() – start:.3f}s”) return result return wrapper @timer def simulated_training(): time.sleep(2) # pretend training a machine learning model here return “model trained” simulated_training() 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 import time from functools import wraps   def timer(func):     @wraps(func)     def wrapper(*args, **kwargs):         start = time.time()         result = func(*args, **kwargs)         print(f“{func.__name__} took {time.time() – start:.3f}s”)         return result     return wrapper   @timer def simulated_training():     time.sleep(2)  # pretend training a machine learning model here     return “model trained”   simulated_training() The key behind this trick is, of course, the definition of the wrapper() function inside timer(func). The majority of examples that follow will use this key pattern: first, we define the key function that can later be used as a decorator for another function. 2. Easier Debugging with @log_calls This is a very handy decorator for debugging purposes. It makes the process of identifying causes for errors or inconsistencies easier, by tracking which functions are called throughout your workflow and which arguments are being passed. A great way to save a bunch of print() statements everywhere! from functools import wraps import pandas as pd def log_calls(func): @wraps(func) def wrapper(*args, **kwargs): print(f”Calling {func.__name__} with {args}, {kwargs}”) return func(*args, **kwargs) return wrapper @log_calls def preprocess_data(df, scale=False): if not isinstance(df, pd.DataFrame): raise TypeError(“Input must be a pandas DataFrame”) return df.copy() # Simple dataset (Pandas DataFrame object) to demonstrate the function data = {‘col1’: [1, 2], ‘col2’: [3, 4]} sample_df = pd.DataFrame(data) preprocess_data(sample_df, scale=True) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 from functools import wraps import pandas as pd   def log_calls(func):     @wraps(func)     def wrapper(*args, **kwargs):         print(f“Calling {func.__name__} with {args}, {kwargs}”)         return func(*args, **kwargs)     return wrapper   @log_calls def preprocess_data(df, scale=False):     if not isinstance(df, pd.DataFrame):         raise TypeError(“Input must be a pandas DataFrame”)     return df.copy()   # Simple dataset (Pandas DataFrame object) to demonstrate the function data = {‘col1’: [1, 2], ‘col2’: [3, 4]} sample_df = pd.DataFrame(data)   preprocess_data(sample_df, scale=True) On first mention, remember to link important libraries for readers: for example, pandas. 3. Caching with @lru_cache This is a pre-defined Python decorator we can directly use by importing it from the functools library. It is suitable to wrap computationally expensive functions — from a recursive Fibonacci computation for a large number to fetching a large dataset — to avoid redundant computations. Useful if we have several heavy functions in computational terms and want to avoid manually implementing caching logic inside all of them one by one. LRU stands for “Least Recently Used”, i.e., a common caching strategy in Python. See also the functools docs. from functools import lru_cache @lru_cache(maxsize=None) def fibonacci(n): if n < 2: return n return fibonacci(n-1) + fibonacci(n-2) print(fibonacci(35)) # Caching this function call makes its execution much faster from functools import lru_cache   @lru_cache(maxsize=None) def fibonacci(n):     if n < 2:         return n     return fibonacci(n–1) + fibonacci(n–2)   print(fibonacci(35))  # Caching this function call makes its execution much faster 4. Data Type Validations This decorator saves you from creating repetitive checks for clean data inputs or inputs belonging to the right type. For instance, below we define a custom decorator called @validate_numeric that customizes the error to throw if the input checked is not from a numeric data type. As a result, validations are kept consistent across different functions and parts of the code, and they are elegantly isolated from the core logic, math, and computations: from functools import wraps def validate_numeric(func): @wraps(func) def wrapper(x): # Accept ints and floats but reject bools (which are a subclass of int). if isinstance(x, bool) or not isinstance(x, (int, float)): raise ValueError(“Input must be numeric”) return func(x) return wrapper @validate_numeric def square_root(x): return x ** 0.5 print(square_root(16)) from functools import wraps   def validate_numeric(func):     @wraps(func)     def wrapper(x):         # Accept ints and floats but reject bools (which are a subclass of int).         if isinstance(x, bool) or not isinstance(x, (int, float)):             raise ValueError(“Input must be numeric”)         return func(x)     return wrapper   @validate_numeric def square_root(x):     return x ** 0.5   print(square_root(16)) 5. Retry on Failure with @retry Sometimes, your code may need to interact with components or establish external connections to APIs, databases, etc. These connections may sometimes fail for several, out-of-control reasons, occasionally even at random. Retrying the process several times in some cases is the way to go and navigate the issue, and the following decorator can be used to apply this “retry on failure” strategy a specified number of times: again, without mixing it with the core logic of your functions. import time, random from functools import wraps def retry(times=3, delay=1): def decorator(func): @wraps(func) def wrapper(*args, **kwargs): last_exc = None

New Delhi: India saw a 5 per cent year-on-year (YoY) increase in refurbished smartphone sales in H1CY25, marking the second-fastest growth globally, a report has said. Apple’s iPhones drove growth, with refurbished iPhone sales in India increasing by 19 per cent, fuelled by strong demand for premium models like the iPhone 13 and iPhone 14 series, according to the report from global market research firm Counterpoint Research. The report indicated that the ongoing premiumisation of the broader smartphone market is now extending to refurbished devices as well, supported by rising consumer awareness, stronger supply chains, and surging demand for high-end models. Africa led global growth with a 6 per cent increase, driven by strong iPhone demand, the report noted. Apple secured the second position in India’s refurbished market, as Samsung maintained the top spot, though experiencing a minor 1 per cent decline. Samsung’s lead was maintained by consistent demand for its Galaxy S22 and S23 models. Additionally, the Samsung Galaxy S22 and S21 were among the top-selling models in India, the report said. Add Zee News as a Preferred Source Southeast Asia’s pre-owned smartphone market also grew 5 per cent YoY in H1 2025, fueled by its large unorganised channels and steady inflow of used devices and components from China. Online platforms are driving the consumer-to-consumer (C2C) market, especially for refurbished smartphones. This growth is fuelled by increasing consumer trust, better supply chains, and the convenience of initiating negotiations and transactions digitally, the research firm said. Organised retailers in India are solidifying buyback initiatives in both online and offline markets by promoting flagship models as reliable and value-driven alternatives. Retailer-driven exchange programs, extended warranty offerings are also driving demand for newer refurbished devices. Apple’s iPhone exports totalled approximately $10 billion, accounting for over 75 per cent of shipments in the first half of the year.

Image by Author Introduction You don’t always need a heavy wrapper, a big client class, or dozens of lines of boilerplate to call a large language model. Sometimes one well-crafted line of Python does all the work: send a prompt, receive a response. That kind of simplicity can speed up prototyping or embedding LLM calls inside scripts or pipelines without architectural overhead. In this article, you’ll see ten Python one-liners that call and interact with LLMs. We will cover: Each snippet comes with a brief explanation and a link to official documentation, so you can verify what’s happening under the hood. By the end, you’ll know not only how to drop in fast LLM calls but also understand when and why each pattern works. Setting Up Before dropping in the one-liners, there are a few things to prepare so they run smoothly: Install required packages (only once): pip install openai anthropic google-generativeai requests httpx pip install openai anthropic google–generativeai requests httpx Ensure your API keys are set in environment variables, never hard-coded in your scripts. For example: export OPENAI_API_KEY=”sk-…” export ANTHROPIC_API_KEY=”claude-yourkey” export GOOGLE_API_KEY=”your_google_key” export OPENAI_API_KEY=“sk-…”   export ANTHROPIC_API_KEY=“claude-yourkey” export GOOGLE_API_KEY=“your_google_key” For local setups (Ollama, LM Studio, vLLM), you need the model server running locally and listening on the correct port (for instance, Ollama’s default REST API runs at http://localhost:11434). All one-liners assume you use the right model name and that the model is either accessible via cloud or locally. With that in place, you can paste each one-liner directly into your Python REPL or script and get a response, subject to quota or local resource limits. Hosted API One-Liners (Cloud Models) Hosted APIs are the easiest way to start using large language models. You don’t have to run a model locally or worry about GPU memory; just install the client library, set your API key, and send a prompt. These APIs are maintained by the model providers themselves, so they’re reliable, secure, and frequently updated. The following one-liners show how to call some of the most popular hosted models directly from Python. Each example sends a simple message to the model and prints the generated response. 1. OpenAI GPT Chat Completion OpenAI’s API gives access to GPT models like GPT-4o and GPT-4o-mini. The SDK handles everything from authentication to response parsing. from openai import OpenAI; print(OpenAI().chat.completions.create(model=”gpt-4o-mini”, messages=[{“role”:”user”,”content”:”Explain vector similarity”}]).choices[0].message.content) from openai import OpenAI; print(OpenAI().chat.completions.create(model=“gpt-4o-mini”, messages=[{“role”:“user”,“content”:“Explain vector similarity”}]).choices[0].message.content) What it does: It creates a client, sends a message to GPT-4o-mini, and prints the model’s reply. Why it works: The openai Python package wraps the REST API cleanly. You only need your OPENAI_API_KEY set as an environment variable. Documentation: OpenAI Chat Completions API 2. Anthropic Claude Anthropic’s Claude models (Claude 3, Claude 3.5 Sonnet, etc.) are known for their long context windows and detailed reasoning. Their Python SDK follows a similar chat-message format to OpenAI’s. from anthropic import Anthropic; print(Anthropic().messages.create(model=”claude-3-5-sonnet”, messages=[{“role”:”user”,”content”:”How does chain of thought prompting work?”}]).content[0].text) from anthropic import Anthropic; print(Anthropic().messages.create(model=“claude-3-5-sonnet”, messages=[{“role”:“user”,“content”:“How does chain of thought prompting work?”}]).content[0].text) What it does: Initializes the Claude client, sends a message, and prints the text of the first response block. Why it works: The .messages.create() method uses a standard message schema (role + content), returning structured output that’s easy to extract. Documentation: Anthropic Claude API Reference 3. Google Gemini Google’s Gemini API (via the google-generativeai library) makes it simple to call multimodal and text models with minimal setup. The key difference is that Gemini’s API treats every prompt as “content generation,” whether it’s text, code, or reasoning. import os, google.generativeai as genai; genai.configure(api_key=os.getenv(“GOOGLE_API_KEY”)); print(genai.GenerativeModel(“gemini-1.5-flash”).generate_content(“Describe retrieval-augmented generation”).text) import os, google.generativeai as genai; genai.configure(api_key=os.getenv(“GOOGLE_API_KEY”)); print(genai.GenerativeModel(“gemini-1.5-flash”).generate_content(“Describe retrieval-augmented generation”).text) What it does: Calls the Gemini 1.5 Flash model to describe retrieval-augmented generation (RAG) and prints the returned text. Why it works: GenerativeModel() sets the model name, and generate_content() handles the prompt/response flow. You just need your GOOGLE_API_KEY configured. Documentation: Google Gemini API Quickstart 4. Mistral AI (REST request) Mistral provides a simple chat-completions REST API. You send a list of messages and receive a structured JSON response in return. import requests, json; print(requests.post(“https://api.mistral.ai/v1/chat/completions”, headers={“Authorization”:”Bearer YOUR_MISTRAL_API_KEY”}, json={“model”:”mistral-tiny”,”messages”:[{“role”:”user”,”content”:”Define fine-tuning”}]}).json()[“choices”][0][“message”][“content”]) import requests, json; print(requests.post(“https://api.mistral.ai/v1/chat/completions”, headers={“Authorization”:“Bearer YOUR_MISTRAL_API_KEY”}, json={“model”:“mistral-tiny”,“messages”:[{“role”:“user”,“content”:“Define fine-tuning”}]}).json()[“choices”][0][“message”][“content”]) What it does: Posts a chat request to Mistral’s API and prints the assistant message. Why it works: The endpoint accepts an OpenAI-style messages array and returns choices -> message -> content.Check out the Mistral API reference and quickstart. 5. Hugging Face Inference API If you host a model or use a public one on Hugging Face, you can call it with a single POST. The text-generation task returns generated text in JSON. import requests; print(requests.post(“https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2”, headers={“Authorization”:”Bearer YOUR_HF_TOKEN”}, json={“inputs”:”Write a haiku about data”}).json()[0][“generated_text”]) import requests; print(requests.post(“https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2”, headers={“Authorization”:“Bearer YOUR_HF_TOKEN”}, json={“inputs”:“Write a haiku about data”}).json()[0][“generated_text”]) What it does: Sends a prompt to a hosted model on Hugging Face and prints the generated text. Why it works: The Inference API exposes task-specific endpoints; for text generation, it returns a list with generated_text.Documentation: Inference API and Text Generation task pages. Local Model One-Liners Running models on your machine gives you privacy and control. You avoid network latency and keep data local. The tradeoff is set up: you need the server running and a model pulled. The one-liners below assume you have already started the local service. 6. Ollama (Local Llama 3 or Mistral) Ollama exposes a simple REST API on localhost:11434. Use /api/generate for prompt-style generation or /api/chat for chat turns. import requests; print(requests.post(“http://localhost:11434/api/generate”, json={“model”:”llama3″,”prompt”:”What is vector search?”}).text) import requests; print(requests.post(“http://localhost:11434/api/generate”, json={“model”:“llama3”,“prompt”:“What is vector search?”}).text) What it does: Sends a generate request to your local Ollama server and prints the raw response text. Why it works: Ollama runs a local HTTP server with endpoints like /api/generate and /api/chat. You must have the app running and the model pulled first. See official API documentation. 7. LM Studio (OpenAI-Compatible Endpoint) LM Studio can serve local models behind OpenAI-style endpoints such as /v1/chat/completions. Start the server from the Developer tab, then call it like any OpenAI-compatible backend. import requests; print(requests.post(“http://localhost:1234/v1/chat/completions”, json={“model”:”phi-3″,”messages”:[{“role”:”user”,”content”:”Explain embeddings”}]}).json()[“choices”][0][“message”][“content”]) import requests; print(requests.post(“http://localhost:1234/v1/chat/completions”, json={“model”:“phi-3”,“messages”:[{“role”:“user”,“content”:“Explain embeddings”}]}).json()[“choices”][0][“message”][“content”]) What it

Amazon Web Services Services Down: Amazon Web Services (AWS) faced a major outage on Monday, disrupting several online services worldwide, including AI tools, e-commerce platforms, popular websites, and online games. The outage affected access to Amazon’s virtual assistant Alexa, the social media app Snapchat, the online game Fortnite, the AI platform ChatGPT, as well as the Epic Games Store and Epic Online Services. Amazon Web Services, Inc., a subsidiary of Amazon, provides on-demand cloud computing platforms and APIs to individuals, businesses, and governments on a metered, pay-as-you-go basis. Amazon reported that it is “investigating increased error rates and latencies for multiple AWS services in the US-EAST-1 Region” and that multiple services are “impacted” by operational issues. Users on social media platform Reddit reported that the Alexa smart assistant is down and unable to respond to queries or complete requests. AWS’ cloud-hosted platforms such as Perplexity, Airtable, Canva, and the McDonald’s app were also affected, according to user reports. Add Zee News as a Preferred Source The cause of the outage hasn’t been confirmed, and it’s unclear when regular service will be restored. Perplexity CEO Aravind Srinivas informed on social media platform X, “Perplexity is down right now. The root cause is an AWS issue. We’re working on resolving it.” AWS outage knocks Amazon, ChatGPT, Alexa and dozens of apps offline. The AWS dashboard first reported issues affecting the US-EAST-1 Region at 3:11AM Eastern Time (ET). “We are actively engaged and working to both mitigate the issue and understand root cause. We will provide an update in 45 minutes, or sooner if we have additional information to share,” Amazon said. Later at 5:27 a.m. ET, Amazon reported “significant signs of recovery,” adding that “most requests should now be succeeding.” We continue to work through a backlog of queued requests, it said. AWS outages in the US-EAST-1 region had caused wide-spread disruptions in 2020, 2021, and 2023, leading to extended downtime for various sites and applications. (With IANS Inputs)

In this article, you will learn how to choose an appropriate time series forecasting model using a clear, four-quadrant decision matrix grounded in data complexity and input dimensionality. Topics we will cover include: The difference between univariate and multivariate time series and why it matters. Which classical and modern models fit best for low vs. high complexity data. Trade-offs among interpretability, scalability, and accuracy across model families. Let’s not waste any more time. A Decision Matrix for Time Series Forecasting ModelsImage by Editor Introduction Time series data have the added complexity of temporal dependencies, seasonality, and possible non-stationarity. Arguably, the most frequent predictive problem to address with time series data is forecasting i.e. predicting future values of a variable like temperature or stock price based on historical observations up to the present. With so many different models for time series forecasting, practitioners might sometimes find it difficult to choose the most suitable approach. This article is designed to help, through the use of a decision matrix accompanied by explanations on when and why to employee different models depending on data characteristics and problem type. The Decision Matrix First up, we introduce the visual matrix that categorizes a set of commonly used time series forecasting models depending on two major criteria or dimensions. A Decision Matrix for Time Series Forecasting ModelsImage by Author Data complexity and structure refers to the overall complexity of the time series dataset being used, in terms of aspects like the presence or absence of stationarity patterns, seasonality, limited vs. significant noise in the data, nonlinearities, and so on. Input dimensionality refers to the fact that, based on input data dimensionality, the time series can be univariate or multivariate i.e. without or with exogenous input attributes, respectively. For instance, a dataset describing daily rides in a public transport system would be an example of a univariate time series, whereas daily or hourly weather recordings including wind speed, temperature, and humidity are an example of a multivariate time series. Univariate vs Multivariate Time SeriesImage by Author These two classification criteria lead us to a taxonomy of time series forecasting models aligned with the matrix displayed above. Let’s now look into each of the four quadrants in more detail. 1. Low-Complexity, Univariate Time Series (Bottom Left) This quadrant encompasses forecasting problems where the historical time series has low complexity — for instance, because it is rather short, it has stable demand (fairly constant over time), or it exhibits simple trends, patterns, or seasonal structure. Normally, these kinds of time series also display approximate stationarity. Suitable and simple models that are normally enough for these problems include Naïve (for extremely simplistic time series data), or slightly more elaborate algorithms or techniques like moving averages and their variants (simple moving average, weighted moving average), the classic among the classics autoregressive integrated moving average (ARIMA), and Holt–Winters. These are all robust models for simple time series datasets, while keeping interpretability and efficiency in forecasts. Meanwhile, due to their simplicity compared to other advanced approaches, their adaptability to issues such as structural breaks or external factors is very limited. 2. Low-Complexity, Multivariate Time Series (Bottom Right) When the time series still has simple patterns but is multivariate — or it is influenced by multiple external factors or regression predictors — it is better to resort to intermediate-complexity models like Dynamic Regression, ARIMA with exogenous variables (ARIMAX), vector autoregression (VAR), or Prophet. These forecasting models can directly incorporate known drivers — such as promotions or pricing effects in customer historical behavior data — into the forecasting, thereby acting as a hybrid between purely time-based forecasting and regression models. These approaches are generally easy to interpret and implement, generating reliable predictions when the underlying dynamics of the dataset remain relatively straightforward. On the other hand, despite being able to incorporate external variables, they still assume relatively simple patterns and relationships and may struggle with nonlinearities or hard-to-understand interactions among variables. 3. High-Complexity, Univariate Time Series (Top Left) Univariate time series exhibiting complex patterns — like irregular trends or multiple seasonal cycles — require using specialized models like TBATS (Trigonometric, Box–Cox transformation, ARMA errors, Trend, and Seasonal components), seasonal ARIMA (SARIMA), or state-space methods such as Kalman filter–based approaches. Aspects like non-stationarity i.e. evolving statistical properties of the data over time, and complex seasonal behaviors can be captured by these models, which makes them suitable for forecasting in scenarios with long-term or irregular series with somewhat “unpredictable” dynamics. Although they outperform other models in coping with internal complexities, these methods are more computationally intensive, and in practice, they often require careful fine-tuning to be precise and generalizable. 4. High-Complexity, Multivariate Time Series (Top Right) Last of the four scenarios, we have contexts with large time series that contain multiple time and/or external variables and present complex or nonlinear dependencies. These challenging scenarios require advanced techniques from the machine learning and deep learning landscape — for example, ensemble methods like Random Forests and XGBoost, recurrent neural networks such as long short-term memory (LSTM) networks, or even deep learning architectures like transformers. Nonetheless, using hybrid approaches is often a wise choice in these contexts. These data-intensive models are superior at capturing complex interactions among variables and are scalable to very large datasets. But on the negative side of things, their requirements are more demanding and they have lower interpretability, along with some risk of overfitting if not enough high-quality data is provided to train them. Wrapping Up This article took a tour of time series forecasting models and methods from the perspective of practical choice. Based on a four-quadrant decision matrix, we outlined the preferred methods to use in four different types of forecasting scenarios, highlighting when to use each group of models and outlining the pros and cons of each.

Diwali 2025: Diwali, the festival of lights, is just around the corner, bringing joy, sweets, and festive cheer to homes across India. As the celebrations begin, many people love sharing warm greetings on WhatsApp before the evening rituals and while lighting up their homes. Stickers have become a fun and easy way to send Diwali wishes without typing long messages. Now, you can use the built-in Diwali sticker pack, as WhatsApp is spreading festive cheer among its Indian users this season. Meta’s instant messaging platform has released a new animated sticker pack specifically for Diwali celebrations. This year, make your WhatsApp chats and status updates glow with colorful Happy Diwali stickers that capture the beauty of diyas, rangolis, and fireworks. Whether you’re greeting family or friends, these vibrant visuals will add a festive touch to every conversation. You can also try using simple AI prompts to create personalized Diwali wishes that feel heartfelt and unique. In this article, we’ll guide you through easy steps to download the best stickers and share five ready-to-use prompts to help you spread light, love, and festive joy. Add Zee News as a Preferred Source How To Download Happy Diwali Stickers For WhatsApp? Step 1: Open WhatsApp on your device and go to the chat where you want to send a sticker. Step 2: Tap the emoji icon in the text box, then choose the sticker icon at the bottom. Step 3: Tap the plus (+) button to open the sticker store. Step 4: Search for “Happy Diwali” stickers and download the packs you like. Step 5: Return to the chat, pick a sticker from the downloaded pack, and tap send. 10 AI Prompts For Quick Diwali Video Wishes Prompt 1: Create a 20-second animated video of a glowing diya lighting up a traditional Indian rangoli at dusk, with sparkling fireworks in the background. Overlay warm text: “Happy Diwali! May prosperity fill your home.” Add soft sitar music and end with a family hugging under fairy lights. Prompt 2: Generate a short clip showing a diverse Indian family lighting lamps together in a vibrant home, transitioning to colorful sweets and gifts. Include upbeat Bollywood-style music and text wishes: “Wishing you joy, health, and endless sweets this Diwali! Prompt 3: Produce a 15-second video of Lakshmi Puja rituals with golden coins raining down on a mandir setup, followed by laughter and dance. Use festive henna patterns as borders, with voiceover: “Deepavali blessings for wealth and happiness.” Prompt 4: Animate a quick scene of kids bursting eco-friendly crackers under a starry night sky, with diyas floating on a river. Add twinkling effects and text: “Celebrate Diwali safely and brightly—Happy Festival of Lights!” Prompt 5: Design a 25-second video blending modern city lights with traditional lamps in Mumbai streets, showing people exchanging sweets. Incorporate rhythmic dhol beats and a message: “From bustling bazaars to your heart, Diwali greetings!” Prompt 6: Create a heartfelt clip of a virtual Diwali card unfolding: fireworks burst to reveal family portraits and rangoli art. Soft flute music plays, ending with: “Even miles apart, our Diwali wishes unite us.” Prompt 7: Generate a fun 20-second reel of animated sweets like laddoos and jalebis dancing around a lit diya, with confetti. Upbeat fusion music and text: “Sweeten your Diwali with love and laughter—Shubh Deepavali!” Prompt 8: Produce a serene video of a woman drawing a kolam at dawn, as the sun rises with blooming lotuses and lights. Gentle bhajan in the background, with overlay: “May Diwali bring peace and new beginnings to your life.” Prompt 9: Animate a 15-second clip of global Indians celebrating Diwali—from Delhi homes to New York parties—with shared video calls and lamps. Energetic music and text: “Diwali knows no borders—Wishing you global joy!” Prompt 10: Create a motivational short: A dark room brightens with countless diyas symbolizing hope, transitioning to success icons like growing lotuses. Inspirational voiceover: “Ignite your dreams this Diwali—Happy New Year ahead!”